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The Bone & Joint Journal
Vol. 106-B, Issue 9 | Pages 978 - 985
1 Sep 2024
Savoie III FH Delvadia BP Tate JP Winter JE Williams GH Sherman WF O’Brien MJ

Rotator cuff tears are common in middle-aged and elderly patients. Despite advances in the surgical repair of rotator cuff tears, the rates of recurrent tear remain high. This may be due to the complexity of the tendons of the rotator cuff, which contributes to an inherently hostile healing environment. During the past 20 years, there has been an increased interest in the use of biologics to complement the healing environment in the shoulder, in order to improve rotator cuff healing and reduce the rate of recurrent tears. The aim of this review is to provide a summary of the current evidence for the use of forms of biological augmentation when repairing rotator cuff tears.

Cite this article: Bone Joint J 2024;106-B(9):978–985.


The Bone & Joint Journal
Vol. 102-B, Issue 12 | Pages 1629 - 1635
1 Dec 2020
Wang Q Sheng N Rui B Chen Y

Aims

The aim of this study was to explore why some calcar screws are malpositioned when a proximal humeral fracture is treated by internal fixation with a locking plate, and to identify risk factors for this phenomenon. Some suggestions can be made of ways to avoid this error.

Methods

We retrospectively identified all proximal humeral fractures treated in our institution between October 2016 and October 2018 using the hospital information system. The patients’ medical and radiological data were collected, and we divided potential risk factors into two groups: preoperative factors and intraoperative factors. Preoperative factors included age, sex, height, weight, body mass index, proximal humeral bone mineral density, type of fracture, the condition of the medial hinge, and medial metaphyseal head extension. Intraoperative factors included the grade of surgeon, neck-shaft angle after reduction, humeral head height, restoration of medial support, and quality of reduction. Adjusted binary logistic regression and multivariate logistic regression models were used to identify pre- and intraoperative risk factors. Area under the curve (AUC) analysis was used to evaluate the discriminative ability of the multivariable model.