Assessment and management of shoulder dislocation
Bibliographic record
Abstract
### What you need to know Shoulder dislocations are painful and have an impact on activities of daily living and participation in sports. Most shoulder dislocations (>95%) occur in the anterior direction and are usually the result of trauma.123 Optimal management can prevent recurrent dislocations and reduce social costs.456 Patients with first time dislocations often receive insufficient information to make a decision about their management.7 Shared decision making must take into consideration the patient’s preferences for surgery or physical therapy, their expectations, and the likelihood of recurrence.6 In this clinical update we present an initial approach for primary care and emergency healthcare providers to assess and manage patients with a traumatic anterior shoulder dislocation. More than 70% of shoulder dislocations occur in men.123 In a cohort study of 16 763 patients who experienced a first time anterior dislocation in the UK, peak incidence was in men aged 16-20 (80.5 per 100 000 person years) and in women aged 61-70 (28.6 per 100 000 person years).3 These peak incidences are similar in other Western countries, such as Canada, the US, and Norway.23 In young patients, shoulder dislocations …
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".