Aims. Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680

Aims. This study aimed to evaluate the clinical application of the PJI-TNM classification for periprosthetic joint infection (PJI) by determining intraobserver and interobserver reliability. To facilitate its use in clinical practice, an educational app was subsequently developed and evaluated. Methods. A total of ten orthopaedic surgeons classified 20 cases of PJI based on the PJI-TNM classification. Subsequently, the classification was re-evaluated using the PJI-TNM app. Classification accuracy was calculated separately for each subcategory (reinfection, tissue and implant condition, non-human cells, and morbidity of the patient). Fleiss’ kappa and Cohen’s kappa were calculated for interobserver and intraobserver reliability, respectively. Results. Overall, interobserver and intraobserver agreements were substantial across the 20 classified cases. Analyses for the variable ‘reinfection’ revealed an almost perfect interobserver and intraobserver agreement with a classification accuracy of 94.8%. The category 'tissue and implant conditions' showed moderate interobserver and substantial intraobserver reliability, while the classification accuracy was 70.8%. For 'non-human cells,' accuracy was 81.0% and interobserver agreement was moderate with an almost perfect intraobserver reliability. The classification accuracy of the variable 'morbidity of the patient' reached 73.5% with a moderate interobserver agreement, whereas the intraobserver agreement was substantial. The application of the app yielded comparable results across all subgroups. Conclusion. The PJI-TNM classification system captures the heterogeneity of PJI and can be applied with substantial inter- and intraobserver reliability. The PJI-TNM educational app aims to facilitate application in clinical practice. A major limitation was the correct assessment of the implant situation. To eliminate this, a re-evaluation according to intraoperative findings is strongly recommended. Cite this article: Bone Joint Res 2024;13(1):19–27

Aims. While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability. Methods. We posed the 20 most Google-searched questions about THA and TKA, plus ten additional postoperative questions, to ChatGPT. Each question was asked twice to evaluate for consistency in quality. Following each response, we responded with, “Please explain so it is easier to understand,” to evaluate ChatGPT’s ability to reduce response reading grade level, measured as Flesch-Kincaid Grade Level (FKGL). Five resident physicians rated the 120 responses on 1 to 5 accuracy and comprehensiveness scales. Additionally, they answered a “yes” or “no” question regarding acceptability. Mean scores were calculated for each question, and responses were deemed acceptable if ≥ four raters answered “yes.”. Results. The mean accuracy and comprehensiveness scores were 4.26 (95% confidence interval (CI) 4.19 to 4.33) and 3.79 (95% CI 3.69 to 3.89), respectively. Out of all the responses, 59.2% (71/120; 95% CI 50.0% to 67.7%) were acceptable. ChatGPT was consistent when asked the same question twice, giving no significant difference in accuracy (t = 0.821; p = 0.415), comprehensiveness (t = 1.387; p = 0.171), acceptability (χ. 2. = 1.832; p = 0.176), and FKGL (t = 0.264; p = 0.793). There was a significantly lower FKGL (t = 2.204; p = 0.029) for easier responses (11.14; 95% CI 10.57 to 11.71) than original responses (12.15; 95% CI 11.45 to 12.85). Conclusion. ChatGPT answered THA and TKA patient questions with accuracy comparable to previous reports of websites, with adequate comprehensiveness, but with limited acceptability as the sole information source. ChatGPT has potential for answering patient questions about THA and TKA, but needs improvement. Cite this article: Bone Jt Open 2024;5(2):139–146

Aims. Accurate skeletal age and final adult height prediction methods in paediatric orthopaedics are crucial for determining optimal timing of growth-guiding interventions and minimizing complications in treatments of various conditions. This study aimed to evaluate the accuracy of final adult height predictions using the central peak height (CPH) method with long leg X-rays and four different multiplier tables. Methods. This study included 31 patients who underwent temporary hemiepiphysiodesis for varus or valgus deformity of the leg between 2014 and 2020. The skeletal age at surgical intervention was evaluated using the CPH method with long leg radiographs. The true final adult height (FH. TRUE. ) was determined when the growth plates were closed. The final height prediction accuracy of four different multiplier tables (1. Bayley and Pinneau; 2. Paley et al; 3. Sanders – Greulich and Pyle (SGP); and 4. Sanders – peak height velocity (PHV)) was then compared using either skeletal age or chronological age. Results. All final adult height predictions overestimated the FH. TRUE. , with the SGP multiplier table having the lowest overestimation and lowest absolute deviation when using both chronological age and skeletal age. There were no significant differences in final height prediction accuracy between using skeletal age and chronological age with PHV (p = 0.652) or SGP multiplier tables (p = 0.969). Adult height predictions with chronological age and SGP (r = 0.769; p ≤ 0.001), as well as chronological age and PHV (r = 0.822; p ≤ 0.001), showed higher correlations with FH. TRUE. than predictions with skeletal age and SGP (r = 0.657; p ≤ 0.001) or skeletal age and PHV (r = 0.707; p ≤ 0.001). Conclusion. There was no significant improvement in adult height prediction accuracy when using the CPH method compared to chronological age alone. The study concludes that there is no advantage in routinely using the CPH method for skeletal age determination over the simple use of chronological age. The findings highlight the need for more accurate methods to predict final adult height in contemporary patient populations. Cite this article: Bone Jt Open 2023;4(10):750–757

Aims. This study aimed to answer the following questions: do 3D-printed models lead to a more accurate recognition of the pattern of complex fractures of the elbow?; do 3D-printed models lead to a more reliable recognition of the pattern of these injuries?; and do junior surgeons benefit more from 3D-printed models than senior surgeons?. Methods. A total of 15 orthopaedic trauma surgeons (seven juniors, eight seniors) evaluated 20 complex elbow fractures for their overall pattern (i.e. varus posterior medial rotational injury, terrible triad injury, radial head fracture with posterolateral dislocation, anterior (trans-)olecranon fracture-dislocation, posterior (trans-)olecranon fracture-dislocation) and their specific characteristics. First, fractures were assessed based on radiographs and 2D and 3D CT scans; and in a subsequent round, one month later, with additional 3D-printed models. Diagnostic accuracy (acc) and inter-surgeon reliability (κ) were determined for each assessment. Results. Accuracy significantly improved with 3D-printed models for the whole group on pattern recognition (acc. 2D/3D. = 0.62 vs acc. 3Dprint. = 0.69; Δacc = 0.07 (95% confidence interval (CI) 0.00 to 0.14); p = 0.025). A significant improvement was also seen in reliability for pattern recognition with the additional 3D-printed models (κ. 2D/3D. = 0.41 (moderate) vs κ. 3Dprint. = 0.59 (moderate); Δκ = 0.18 (95% CI 0.14 to 0.22); p ≤ 0.001). Accuracy was comparable between junior and senior surgeons with the 3D-printed model (acc. junior. = 0.70 vs acc. senior. = 0.68; Δacc = -0.02 (95% CI -0.17 to 0.13); p = 0.904). Reliability was also comparable between junior and senior surgeons without the 3D-printed model (κ. junior. = 0.39 (fair) vs κ. senior. = 0.43 (moderate); Δκ = 0.03 (95% CI -0.03 to 0.10); p = 0.318). However, junior surgeons showed greater improvement regarding reliability than seniors with 3D-printed models (κ. junior. = 0.65 (substantial) vs κ. senior. = 0.54 (moderate); Δκ = 0.11 (95% CI 0.04 to 0.18); p = 0.002). Conclusion. The use of 3D-printed models significantly improved the accuracy and reliability of recognizing the pattern of complex fractures of the elbow. However, the current long printing time and non-reusable materials could limit the usefulness of 3D-printed models in clinical practice. They could be suitable as a reusable tool for teaching residents. Cite this article: Bone Joint J 2023;105-B(1):56–63

Aims. The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support. Methods. The dataset consisted of 26,121 anonymized anterior-posterior (AP) and lateral standard view radiographs of the wrist, with and without DRF. The convolutional neural network (CNN) model was trained to detect the presence of a DRF by comparing the radiographs containing a fracture to the inconspicuous ones. A total of 11 physicians (six surgeons in training and five hand surgeons) assessed 200 pairs of randomly selected digital radiographs of the wrist (AP and lateral) for the presence of a DRF. The same images were first evaluated without, and then with, the support of the CNN model, and the diagnostic accuracy of the two methods was compared. Results. At the time of the study, the CNN model showed an area under the receiver operating curve of 0.97. AI assistance improved the physician’s sensitivity (correct fracture detection) from 80% to 87%, and the specificity (correct fracture exclusion) from 91% to 95%. The overall error rate (combined false positive and false negative) was reduced from 14% without AI to 9% with AI. Conclusion. The use of a CNN model as a second opinion can improve the diagnostic accuracy of DRF detection in the study setting. Cite this article: Bone Joint Res 2024;13(10):588–595

Aims. The aim of this study was to assess the accuracy of pedicle screw placement, as well as intraoperative factors, radiation exposure, and complication rates in adult patients with degenerative disorders of the thoracic and lumbar spines who have undergone robotic-navigated spinal surgery using a contemporary system. Methods. The authors reviewed the prospectively collected data on 196 adult patients who had pedicle screws implanted with robot-navigated assistance (RNA) using the Mazor X Stealth system between June 2019 and March 2022. Pedicle screws were implanted by one experienced spinal surgeon after completion of a learning period. The accuracy of pedicle screw placement was determined using intraoperative 3D fluoroscopy. Results. A total of 1,123 pedicle screws were implanted: 1,001 screws (89%) were placed robotically, 63 (6%) were converted from robotic placement to a freehand technique, and 59 (5%) were planned to be implanted freehand. Of the robotically placed screws, 942 screws (94%) were determined to be Gertzbein and Robbins grade A with median deviation of 0.8 mm (interquartile range 0.4 to 1.6). Skive events were noted with 20 pedicle screws (1.8%). No adverse clinical sequelae were noted in the 90-day follow-up. The mean fluoroscopic exposure per screw was 4.9 seconds (SD 3.8). Conclusion. RNA is highly accurate and reliable, with a low rate of abandonment once mastered. No adverse clinical sequelae occurred after implanting a large series of pedicle screws using the latest generation of RNA. Understanding of patient-specific anatomical features and the real-time intraoperative identification of risk factors for suboptimal screw placement have the potential to improve accuracy further. Cite this article: Bone Joint J 2023;105-B(5):543–550

Aims. The diagnosis of periprosthetic joint infection (PJI) can be challenging as the symptoms are similar to other conditions, and the markers used for diagnosis have limited sensitivity and specificity. Recent research has suggested using blood cell ratios, such as platelet-to-volume ratio (PVR) and platelet-to-lymphocyte ratio (PLR), to improve diagnostic accuracy. The aim of the study was to further validate the effectiveness of PVR and PLR in diagnosing PJI. Methods. A retrospective review was conducted to assess the accuracy of different marker combinations for diagnosing chronic PJI. A total of 573 patients were included in the study, of which 124 knees and 122 hips had a diagnosis of chronic PJI. Complete blood count and synovial fluid analysis were collected. Recently published blood cell ratio cut-off points were applied to receiver operating characteristic curves for all markers and combinations. The area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values were calculated. Results. The results of the analysis showed that the combination of ESR, CRP, synovial white blood cell count (Syn. WBC), and polymorphonuclear neutrophil percentage (PMN%) with PVR had the highest AUC of 0.99 for knees, with sensitivity of 97.73% and specificity of 100%. Similarly, for hips, this combination had an AUC of 0.98, sensitivity of 96.15%, and specificity of 100.00%. Conclusion. This study supports the use of PVR calculated from readily available complete blood counts, combined with established markers, to improve the accuracy in diagnosing chronic PJI in both total hip and knee arthroplasties. Cite this article: Bone Jt Open 2023;4(11):881–888

Aims. The aim of this study was to determine the rate of indocyanine green (ICG) staining of bone and soft-tissue tumours, as well as the stability and accuracy of ICG fluorescence imaging in detecting tumour residuals during surgery for bone and soft-tissue tumours. Methods. ICG fluorescence imaging was performed during surgery in 34 patients with bone and soft-tissue tumours. ICG was administered intravenously at a dose of 2 mg/kg over a period of 60 minutes on the day prior to surgery. The tumour stain rate and signal-to-background ratio of each tumour were post hoc analyzed. After tumour resection, the tumour bed was scanned to locate sites with fluorescence residuals, which were subsequently inspected and biopsied. Results. The overall tumour stain rate was 88% (30/34 patients), and specific stain rates included 90% for osteosarcomas and 92% for giant cell tumours. For malignant tumours, the overall stain rate was 94%, while it was 82% for benign tumours. The ICG tumour stain was not influenced by different pathologies, such as malignant versus benign pathology, the reception (or lack thereof) of neoadjuvant chemotherapies, the length of time between drug administration and surgery, the number of doses of denosumab for patients with giant cell tumours, or the tumour response to neoadjuvant chemotherapy. The overall accuracy rate of successfully predicting tumour residuals using fluorescence was 49% (23/47 pieces of tissue). The accuracy rate after en bloc resection was significantly lower than that after piecemeal resection (16% vs 71%; p < 0.001). Conclusion. A high percentage of bone and soft-tissue tumours can be stained by ICG and the tumour staining with ICG was stable. This approach can be used in both benign and malignant tumours, regardless of whether neoadjuvant chemotherapy is adopted. The technique is also useful to detect tumour residuals in the wound, especially in patients undergoing piecemeal resection. Cite this article: Bone Joint J 2023;105-B(5):551–558

Aims. The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs. Methods. The CNN was trained and tested on radiographs sourced from 11 hospitals in Australia and externally validated on radiographs from the Netherlands. Each radiograph was paired with corresponding CT scans to serve as the reference standard based on dual independent evaluation by trained researchers and attending orthopaedic surgeons. Presence of a fracture, classification (non- to minimally displaced; two-part, multipart, and glenohumeral dislocation), and four characteristics were determined on 2D and 3D CT scans and subsequently allocated to each series of radiographs. Fracture characteristics included greater tuberosity displacement ≥ 1 cm, NSA ≤ 100°, shaft translation (0% to < 75%, 75% to 95%, > 95%), and the extent of articular involvement (0% to < 15%, 15% to 35%, or > 35%). Results. For detection and classification, the algorithm was trained on 1,709 radiographs (n = 803), tested on 567 radiographs (n = 244), and subsequently externally validated on 535 radiographs (n = 227). For characterization, healthy shoulders and glenohumeral dislocation were excluded. The overall accuracy for fracture detection was 94% (area under the receiver operating characteristic curve (AUC) = 0.98) and for classification 78% (AUC 0.68 to 0.93). Accuracy to detect greater tuberosity fracture displacement ≥ 1 cm was 35.0% (AUC 0.57). The CNN did not recognize NSAs ≤ 100° (AUC 0.42), nor fractures with ≥ 75% shaft translation (AUC 0.51 to 0.53), or with ≥ 15% articular involvement (AUC 0.48 to 0.49). For all objectives, the model’s performance on the external dataset showed similar accuracy levels. Conclusion. CNNs proficiently rule out proximal humerus fractures on plain radiographs. Despite rigorous training methodology based on CT imaging with multi-rater consensus to serve as the reference standard, artificial intelligence-driven classification is insufficient for clinical implementation. The CNN exhibited poor diagnostic ability to detect greater tuberosity displacement ≥ 1 cm and failed to identify NSAs ≤ 100°, shaft translations, or articular fractures. Cite this article: Bone Joint J 2024;106-B(11):1348–1360

Aims. Achieving accurate implant positioning and restoring native hip biomechanics are key surgeon-controlled technical objectives in total hip arthroplasty (THA). The primary objective of this study was to compare the reproducibility of the planned preoperative centre of hip rotation (COR) in patients undergoing robotic arm-assisted THA versus conventional THA. Methods. This prospective randomized controlled trial (RCT) included 60 patients with symptomatic hip osteoarthritis undergoing conventional THA (CO THA) versus robotic arm-assisted THA (RO THA). Patients in both arms underwent pre- and postoperative CT scans, and a patient-specific plan was created using the robotic software. The COR, combined offset, acetabular orientation, and leg length discrepancy were measured on the pre- and postoperative CT scanogram at six weeks following surgery. Results. There were no significant differences for any of the baseline characteristics including spinopelvic mobility. The absolute error for achieving the planned horizontal COR was median 1.4 mm (interquartile range (IQR) 0.87 to 3.42) in RO THA versus 4.3 mm (IQR 3 to 6.8; p < 0.001); vertical COR mean 0.91 mm (SD 0.73) in RO THA versus 2.3 mm (SD 1.3; p < 0.001); and combined offset median 2 mm (IQR 0.97 to 5.45) in RO THA versus 3.9 mm (IQR 2 to 7.9; p = 0.019). Improved accuracy was observed with RO THA in achieving the desired acetabular component positioning (root mean square error for anteversion and inclination was 2.6 and 1.3 vs 8.9 and 5.3, repectively) and leg length (mean 0.6 mm vs 1.4 mm; p < 0.001). Patient-reported outcome measures were comparable between the two groups at baseline and one year. Participants in the RO THA group needed fewer physiotherapy sessions postoperatively (median six (IQR 4.5 to 8) vs eight (IQR 6 to 11; p = 0.005). Conclusion. This RCT suggested that robotic-arm assistance in THA was associated with improved accuracy in restoring the native COR, better preservation of the combined offset, leg length correction, and superior accuracy in achieving the desired acetabular component positioning. Further evaluation through long-term and registry data is necessary to assess whether these findings translate into improved implant survival and functional outcomes. Cite this article: Bone Joint J 2024;106-B(4):324–335

Aims. Low-grade central osteosarcoma (LGCOS), a rare type of osteosarcoma, often has misleading radiological and pathological features that overlap with those of other bone tumours, thereby complicating diagnosis and treatment. We aimed to analyze the clinical, radiological, and pathological features of patients with LGCOS, with a focus on diagnosis, treatment, and outcomes. Methods. We retrospectively analyzed the medical records of 49 patients with LGCOS (Broder’s grade 1 to 2) treated between January 1985 and December 2017 in a single institute. We examined the presence of malignant features on imaging (periosteal reaction, cortical destruction, soft-tissue invasion), the diagnostic accuracy of biopsy, surgical treatment, and oncological outcome. Results. Based on imaging, 35 of 49 patients (71.4%) exhibited malignant features. Overall, 40 of 49 patients (81.6%) had undergone a biopsy before en-bloc resection: 27 of 40 patients (67.5%) were diagnosed on the first biopsy, which was more accurate when carried out by open rather than needle biopsy (91.3% vs 35.3% diagnostic accuracy, respectively; p < 0.001). Of the 40 patients treated by en-bloc resection, surgical margins were wide in 38 (95.0%) and marginal in two (5.0%). Furthermore, nine of 49 patients (18.4%) underwent curettage (intralesional margin) without previous biopsy. All patients with a positive margin developed local recurrence. Distant metastases occurred in five of 49 patients (10.2%). The mean five-year overall survival (OS) and distant relapse-free survival (D-RFS) were 89.3% (SD 5.1%) and 85.7% (SD 5.5%), respectively. Univariate analysis showed that the occurrence of distant metastasis was a poor prognostic factor for OS (hazard ratio 11.54, 95% confidence interval (CI) 1.92 to 69.17; p < 0.001). Local recurrence was a poor prognostic factor for D-RFS (HR 8.72, 95% CI 1.69 to 45.0; p = 0.002). Conclusion. The diagnosis of LGCOS can be challenging because it may present with non-malignant features and has a low diagnostic accuracy on biopsy. If precisely diagnosed, LGCOS can be successfully treated by surgical excision with wide margins. Cite this article: Bone Joint J 2024;106-B(1):99–106

Aims. The benefit of MRI in the preoperative assessment of scaphoid proximal fragment vascularity remains controversial. The purpose of this study is to compare preoperative MRI findings to intraoperative bleeding of the proximal scaphoid. Methods. A retrospective review of 102 patients who underwent surgery for scaphoid nonunion between January 2000 and December 2020 at a single institution were identified. Inclusion criteria were: isolated scaphoid nonunion; preoperative MRI assessing the proximal fragment vascularity; and operative details of the vascularity of the proximal fragment with the tourniquet deflated. MRI results and intraoperative findings were dichotomized as either ‘yes’ or ‘no’ for the presence of vascularity. A four-fold contingency table was used to analyze the utility of preoperative MRI with 95% confidence intervals. Relative risk was calculated for subgroups to analyze the association between variables and MRI accuracy. Results. Preoperative MRI identified 55 proximal scaphoid fragments with ischaemia and 47 with vascularized proximal fragments. After the proximal fragment was prepared, the tourniquet was deflated and assessed for bleeding; 63 proximal fragments had no bleeding and 39 demonstrated bleeding. MRI was not reliable or accurate in the assessment of proximal fragment vascularity when compared with intraoperative assessment of bleeding. No patient or MRI factors were identified to have a statistical impact on MRI accuracy. Conclusion. Current preoperative MRI protocols and diagnostic criteria do not provide a high degree of correlation with observed intraoperative assessment of proximal fragment bleeding. While preoperative MRI may assist in surgical planning, intraoperative assessment remains the best means for assessing proximal fragment vascularity in scaphoid nonunion. Future efforts should focus on the development of objective measures of osseous blood flow that may be performed intraoperatively. Cite this article: Bone Joint J 2023;105-B(6):657–662

Aims. Manual impaction, with a mallet and introducer, remains the standard method of installing cementless acetabular cups during total hip arthroplasty (THA). This study aims to quantify the accuracy and precision of manual impaction strikes during the seating of an acetabular component. This understanding aims to help improve impaction surgical techniques and inform the development of future technologies. Methods. Posterior approach THAs were carried out on three cadavers by an expert orthopaedic surgeon. An instrumented mallet and introducer were used to insert cementless acetabular cups. The motion of the mallet, relative to the introducer, was analyzed for a total of 110 strikes split into low-, medium-, and high-effort strikes. Three parameters were extracted from these data: strike vector, strike offset, and mallet face alignment. Results. The force vector of the mallet strike, relative to the introducer axis, was misaligned by an average of 18.1°, resulting in an average wasted strike energy of 6.1%. Furthermore, the mean strike offset was 19.8 mm from the centre of the introducer axis and the mallet face, relative to the introducer strike face, was misaligned by a mean angle of 15.2° from the introducer strike face. Conclusion. The direction of the impact vector in manual impaction lacks both accuracy and precision. There is an opportunity to improve this through more advanced impaction instruments or surgical training. Cite this article: Bone Joint Res 2024;13(4):193–200

Aims. Glenoid bone loss is a significant problem in the management of shoulder instability. The threshold at which the bone loss is considered “critical” requiring bony reconstruction has steadily dropped and is now approximately 15%. This necessitates accurate measurement in order that the correct operation is performed. CT scanning is the most commonly used modality and there are a number of techniques described to measure the bone loss however few have been validated. The aim of this study was to assess the accuracy of the most commonly used techniques for measuring glenoid bone loss on CT. Methods. Anatomically accurate models with known glenoid diameter and degree of bone loss were used to determine the mathematical and statistical accuracy of six of the most commonly described techniques (relative diameter, linear ipsilateral circle of best fit (COBF), linear contralateral COBF, Pico, Sugaya, and circle line methods). The models were prepared at 13.8%, 17.6%, and 22.9% bone loss. Sequential CT scans were taken and randomized. Blinded reviewers made repeated measurements using the different techniques with a threshold for theoretical bone grafting set at 15%. Results. At 13.8%, only the Pico technique measured under the threshold. At 17.6% and 22.9% bone loss all techniques measured above the threshold. The Pico technique was 97.1% accurate, but had a high false-negative rate and poor sensitivity underestimating the need for grafting. The Sugaya technique had 100% specificity but 25% of the measurements were incorrectly above the threshold. A contralateral COBF underestimates the area by 16% and the diameter by 5 to 7%. Conclusion. No one method stands out as being truly accurate and clinicians need to be aware of the limitations of their chosen technique. They are not interchangeable, and caution must be used when reading the literature as comparisons are not reliable. Cite this article: Bone Jt Open 2023;4(7):478–489

Aims. This systematic review aims to identify 3D predictors derived from biplanar reconstruction, and to describe current methods for improving curve prediction in patients with mild adolescent idiopathic scoliosis. Methods. A comprehensive search was conducted by three independent investigators on MEDLINE, PubMed, Web of Science, and Cochrane Library. Search terms included “adolescent idiopathic scoliosis”,“3D”, and “progression”. The inclusion and exclusion criteria were carefully defined to include clinical studies. Risk of bias was assessed with the Quality in Prognostic Studies tool (QUIPS) and Appraisal tool for Cross-Sectional Studies (AXIS), and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. In all, 915 publications were identified, with 377 articles subjected to full-text screening; overall, 31 articles were included. Results. Torsion index (TI) and apical vertebral rotation (AVR) were identified as accurate predictors of curve progression in early visits. Initial TI > 3.7° and AVR > 5.8° were predictive of curve progression. Thoracic hypokyphosis was inconsistently observed in progressive curves with weak evidence. While sagittal wedging was observed in mild curves, there is insufficient evidence for its correlation with curve progression. In curves with initial Cobb angle < 25°, Cobb angle was a poor predictor for future curve progression. Prediction accuracy was improved by incorporating serial reconstructions in stepwise layers. However, a lack of post-hoc analysis was identified in studies involving geometrical models. Conclusion. For patients with mild curves, TI and AVR were identified as predictors of curve progression, with TI > 3.7° and AVR > 5.8° found to be important thresholds. Cobb angle acts as a poor predictor in mild curves, and more investigations are required to assess thoracic kyphosis and wedging as predictors. Cumulative reconstruction of radiographs improves prediction accuracy. Comprehensive analysis between progressive and non-progressive curves is recommended to extract meaningful thresholds for clinical prognostication. Cite this article: Bone Jt Open 2024;5(3):243–251

Aims. Clinical management of open fractures is challenging and frequently requires complex reconstruction procedures. The Gustilo-Anderson classification lacks uniform interpretation, has poor interobserver reliability, and fails to account for injuries to musculotendinous units and bone. The Ganga Hospital Open Injury Severity Score (GHOISS) was designed to address these concerns. The major aim of this review was to ascertain the evidence available on accuracy of the GHOISS in predicting successful limb salvage in patients with mangled limbs. Methods. We searched electronic data bases including PubMed, CENTRAL, EMBASE, CINAHL, Scopus, and Web of Science to identify studies that employed the GHOISS risk tool in managing complex limb injuries published from April 2006, when the score was introduced, until April 2021. Primary outcome was the measured sensitivity and specificity of the GHOISS risk tool for predicting amputation at a specified threshold score. Secondary outcomes included length of stay, need for plastic surgery, deep infection rate, time to fracture union, and functional outcome measures. Diagnostic test accuracy meta-analysis was performed using a random effects bivariate binomial model. Results. We identified 1,304 records, of which six prospective cohort studies and two retrospective cohort studies evaluating a total of 788 patients were deemed eligible for inclusion. A diagnostic test meta-analysis conducted on five cohort studies, with 474 participants, showed that GHOISS at a threshold score of 14 has a pooled sensitivity of 93.4% (95% confidence interval (CI) 78.4 to 98.2) and a specificity of 95% (95% CI 88.7 to 97.9) for predicting primary or secondary amputations in people with complex lower limb injuries. Conclusion. GHOISS is highly accurate in predicting success of limb salvage, and can inform management and predict secondary outcomes. However, there is a need for high-quality multicentre trials to confirm these findings and investigate the effectiveness of the score in children, and in predicting secondary amputations. Cite this article: Bone Joint J 2023;105-B(1):21–28

Aims. As an alternative to external fixators, intramedullary lengthening nails (ILNs) can be employed for distraction osteogenesis. While previous studies have demonstrated that typical complications of external devices, such as soft-tissue tethering, and pin site infection can be avoided with ILNs, there is a lack of studies that exclusively investigated tibial distraction osteogenesis with motorized ILNs inserted via an antegrade approach. Methods. A total of 58 patients (median age 17 years (interquartile range (IQR) 15 to 21)) treated by unilateral tibial distraction osteogenesis for a median leg length discrepancy of 41 mm (IQR 34 to 53), and nine patients with disproportionate short stature treated by bilateral simultaneous tibial distraction osteogenesis, with magnetically controlled motorized ILNs inserted via an antegrade approach, were retrospectively analyzed. The median follow-up was 37 months (IQR 30 to 51). Outcome measurements were accuracy, precision, reliability, bone healing, complications, and patient-reported outcome assessed by the Limb Deformity-Scoliosis Research Society Score (LD-SRS-30). Results. A median tibial distraction of 44 mm (IQR 31 to 49) was achieved with a mean distraction index of 0.5 mm/day (standard deviation 0.13) and median consolidation index of 41.2 days/cm (IQR 34 to 51). Accuracy, precision, and reliability were 91%, 92%, and 97%, respectively. New temporary range of motion limitations occurred in 51% of segments (34/67). Distraction-related equinus deformity treated by Achilles tendon lengthening was the most common major complication recorded in 16% of segments (11/67). In 95% of patients (55/58) the distraction goal was achieved with 42% unplanned additional interventions per segment (28/67). The median postoperative LD-SRS-30 score was 4.0 (IQR 3.6 to 4.3). Conclusion. Tibial distraction osteogenesis using motorized ILNs inserted via an antegrade approach appears to be a reliable and precise procedure. Temporary joint stiffness of the knee or ankle should be expected in up to every second patient. A high rate and wide range of complications of variable severity should be anticipated. Cite this article: Bone Joint J 2024;106-B(3):293–302

Aims. Serum inflammatory parameters are widely used to aid in diagnosing a periprosthetic joint infection (PJI). Due to their limited performances in the literature, novel and more accurate biomarkers are needed. Serum albumin-to-globulin ratio (AGR) and serum CRP-to-albumin ratio (CAR) have previously been proposed as potential new parameters, but results were mixed. The aim of this study was to assess the diagnostic accuracy of AGR and CAR in diagnosing PJI and to compare them to the established and widely used marker CRP. Methods. From 2015 to 2022, a consecutive series of 275 cases of revision total hip (n = 129) and knee arthroplasty (n = 146) were included in this retrospective cohort study. Based on the 2021 European Bone and Joint Infection Society (EBJIS) definition, 144 arthroplasties were classified as septic. Using receiver operating characteristic curve (ROC) analysis, the ideal thresholds and diagnostic performances were calculated. The areas under the curve (AUCs) were compared using the z-test. Results. AGR, CAR, and CRP were associated with PJI (p < 0.001). Sensitivities were 62.5% (95% CI 54.3 to 70.0), 73.6% (95% CI 65.8 to 80.1), and 71.5% (95% CI 63.6 to 78.3), respectively. Specificities were calculated with 84.7% (95% CI 77.5 to 89.9), 86.3% (95% CI 79.2 to 91.2), and 87.8% (95% CI 80.9 to 92.4), respectively. The AUC of CRP (0.797 (95% CI 0.750 to 0.843)) was significantly higher than the AUC of AGR (0.736 (95% CI 0.686 to 0.786), p < 0.001), and similar to AUC of CAR (0.799 (95% CI 0.753 to 0.846), p = 0.832). Decreased sensitivities were observed in PJIs caused by low-virulence organisms (AGR: 60%, CAR: 78%) compared to high-virulence pathogens (AGR: 80%, p = 0.042; CAR: 88%, p = 0.158). Higher sensitivities were seen in acute haematogenous (AGR: 83%, CAR: 96%) compared to chronic PJIs (AGR: 54%, p = 0.001; CAR: 65%, p < 0.001). Conclusion. Serum AGR and CAR showed limited diagnostic accuracy (especially in low-grade and chronic infections) and did not outperform the established marker CRP in our study. Hence, neither parameter can be recommended as an additional tool for diagnosing PJI. Cite this article: Bone Joint Res 2024;13(8):372–382

Aims. The early diagnosis of cauda equina syndrome (CES) is crucial for a favourable outcome. Several studies have reported the use of an ultrasound scan of the bladder as an adjunct to assess the minimum post-void residual volume of urine (mPVR). However, variable mPVR values have been proposed as a threshold without consensus on a value for predicting CES among patients with relevant symptoms and signs. The aim of this study was to perform a meta-analysis and systematic review of the published evidence to identify a threshold mPVR value which would provide the highest diagnostic accuracy in patients in whom the diagnosis of CES is suspected. Methods. The search strategy used electronic databases (PubMed, Medline, EMBASE, and AMED) for publications between January 1996 and November 2021. All studies that reported mPVR in patients in whom the diagnosis of CES was suspected, followed by MRI, were included. Results. A total of 2,115 studies were retrieved from the search. Seven fulfilled the inclusion criteria. These included 1,083 patients, with data available from 734 being available for meta-analysis. In 125 patients, CES was confirmed by MRI. The threshold value of mPVR reported in each study varied and could be categorized into 100 ml, 200 ml, 300 ml, and 500 ml. From the meta-analysis, 200 ml had the highest diagnostic accuracy, with 82% sensitivity (95% confidence interval (CI) 0.72 to 0.90) and 65% specificity (95% CI 0.70 to 0.90). When compared using summative receiver operating characteristic curves, mPVR of 200 ml was superior to other values in predicting the radiological confirmation of CES. Conclusion. mPVR is a useful tool when assessing patients in whom the diagnosis of CES is suspected. Compared with other values a mPVR of 200 ml had superior sensitivity, specificity, and positive and negative predictive values. In a patient with a suggestive history and clinical findings, a mPVR of > 200 ml should further raise the suspicion of CES. Caution is recommended when considering the mPVR in isolation and using it as an ‘exclusion tool’, and it should only be used as an adjunct to a full clinical assessment. Cite this article: Bone Joint J 2023;105-B(5):487–495