Treatment Preferences for Displaced Three- and Four-Part Proximal Humerus Fractures
Bibliographic record
Abstract
OBJECTIVES: To assess the treatment preferences of orthopaedic surgeons for displaced three- and four-part proximal humerus fractures. DESIGN: Cross-sectional survey. SETTING: Academic and nonacademic institutions across Canada. PATIENTS/PARTICIPANTS: Members of the Canadian Orthopedic Trauma Society and the Joint Orthopedic Initiative for National Trials of the Shoulder. INTERVENTION: Mailed questionnaire requesting treatment preferences for eight clinical scenarios with differing patient age (45 or 68 years), patient activity level (active/healthy or frail/low demand), and fracture type (Neer III or IV). MAIN OUTCOME MEASUREMENTS: Treatment preference for each clinical scenario using a five-level ordinal scale. RESULTS: Thirty-six completed questionnaires were included in the analysis. Internal fixation, particularly with locking plates, was the preferred treatment for young patients regardless of fracture type or activity level. Treatment preferences for elderly patients demonstrated the least consensus. Hemiarthroplasty, locked plating, and nonoperative management all received high treatment preferences depending on the fracture type and activity level of the patient. CONCLUSIONS: This survey quantifies treatment preferences for a wide range of strategies used to manage displaced proximal humerus fractures. The results of this survey suggest that, despite consulting orthopaedic trauma and shoulder experts, a wide range of treatments appear acceptable for displaced fractures in the elderly. Prospective clinical trials are needed to guide effective treatment decisions for these patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".