Mean Glenoid Defect Size and Location Associated With Anterior Shoulder Instability
Bibliographic record
Abstract
BACKGROUND: There is a strong correlation between glenoid defect size and recurrent anterior shoulder instability. A better understanding of glenoid defects could lead to improved treatments and outcomes. PURPOSE: To (1) determine the rate of reporting numeric measurements for glenoid defect size, (2) determine the consistency of glenoid defect size and location reported within the literature, (3) define the typical size and location of glenoid defects, and (4) determine whether a correlation exists between defect size and treatment outcome. STUDY DESIGN: Systematic review; Level of evidence, 4. METHODS: PubMed, Ovid, and Cochrane databases were searched for clinical studies measuring glenoid defect size or location. We excluded studies with defect size requirements or pathology other than anterior instability and studies that included patients with known prior surgery. Our search produced 83 studies; 38 studies provided numeric measurements for glenoid defect size and 2 for defect location. RESULTS: From 1981 to 2000, a total of 5.6% (1 of 18) of the studies reported numeric measurements for glenoid defect size; from 2001 to 2014, the rate of reporting glenoid defects increased to 58.7% (37 of 63). Fourteen studies (n = 1363 shoulders) reported defect size ranges for percentage loss of glenoid width, and 9 studies (n = 570 shoulders) reported defect size ranges for percentage loss of glenoid surface area. According to 2 studies, the mean glenoid defect orientation was pointing toward the 3:01 and 3:20 positions on the glenoid clock face. CONCLUSION: Since 2001, the rate of reporting numeric measurements for glenoid defect size was only 58.7%. Among studies reporting the percentage loss of glenoid width, 23.6% of shoulders had a defect between 10% and 25%, and among studies reporting the percentage loss of glenoid surface area, 44.7% of shoulders had a defect between 5% and 20%. There is significant variability in the way glenoid bone loss is measured, calculated, and reported.
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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 | 0.011 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".