Indications and outcomes of glenoid osteotomy for posterior shoulder instability: a systematic review
Bibliographic record
Abstract
Background There is limited evidence examining glenoid osteotomy as a treatment for posterior shoulder instability. Methods A search of Medline, Embase, PubMed and Cochrane Central Register of Controlled Trials was conducted from the date of origin to 28th November 2019. Nine out of 3,408 retrieved studies met the inclusion criteria and quality was assessed using the Methodological Index for Non-randomized Studies tool. Results In 356 shoulders, the main indication for osteotomy was excessive glenoid retroversion (greater than or equal to approximately −10°). The mean preoperative glenoid version was −15° (range, −35° to −5°). Post-operatively, the mean glenoid version was −6° (range, −28° to 13°) and an average correction of 10° (range, −1° to 30°) was observed. Range of motion increased significantly in most studies and all standardized outcome scores (Rowe, Constant–Murley, Oxford instability, Japan Shoulder Society Shoulder Instability Scoring and mean shoulder value) improved significantly with high rates of patient satisfaction (85%). A high complication rate (34%, n = 120) was reported post-surgery, with frequent cases of persistent instability (20%, n = 68) and fractures (e.g., glenoid neck and acromion) (4%, n = 12). However, the revision rate was low (0.6%, n = 2). Conclusion Glenoid osteotomy is an appropriate treatment for posterior shoulder instability secondary to excessive glenoid retroversion. However, the high rate of persistent instability should be considered when making treatment decisions. Level of Evidence: Systematic review; Level 4
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".