Characterizing the Practices of Canadian Orthopedic Surgeons in the Management of patients With Anterior Glenohumeral Instability
Bibliographic record
Abstract
OBJECTIVE: To determine the practice patterns of Canadian orthopedic surgeons in the management of patients with anterior glenohumeral instability (AGHI). DESIGN: Cross-sectional survey. SETTING: Canada. PATIENTS OR OTHER PARTICIPANTS: Canadian orthopedic surgeons with membership in the Canadian Orthopedic Association or Canadian Shoulder and Elbow Surgeon group who had managed at least 1 patient with AGHI in the previous year. INTERVENTIONS: A survey including demographics and questions on the management of patients with AGHI was completed. Statistical comparisons (χ 2 ) were completed with responses stratified using the instability severity index score (ISIS) in practice, years of practice, and surgical volumes. MAIN OUTCOME MEASURES: Summary statistics were compiled, and response frequencies were considered for consensus (75%). Case series responses were stratified on use of the ISIS in practice, years of experience, and annual procedure volumes (χ 2 , P < 0.05). RESULTS: Eighty orthopedic surgeons responded, with consensus on areas of diagnostic workup of AGHI, nonoperative management, and operative techniques. There was no consensus on indications for soft tissue and bony augmentation or postoperative management. There was no difference in practices based on the use of ISIS, years in practice, or surgical volumes. CONCLUSIONS: Canadian orthopedic surgeons manage AGHI consistently with consensus achieved in preoperative diagnostics and operative techniques, although debate remains as to the indications for soft tissue and bony augmentation procedures.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".