1632 Clinical predictors of fracture in patients with shoulder dislocation: systematic review of diagnostic test accuracy studies
Bibliographic record
Abstract
Aims, Objectives and Background Pre-reduction radiographs are conventionally used to exclude important fracture before attempts to reduce a dislocated shoulder in the Emergency Department. However, this step increases cost, exposes patients to ionising radiation, and might delay closed reduction. Some studies have suggested that pre-reduction imaging may be omitted for a sub-group of patients with shoulder dislocations. The objective was to determine whether clinical predictors can identify patients that might safely undergo closed reduction of a dislocated shoulder without pre-reduction radiographs. Method and Design A systematic review and meta-analysis of diagnostic test accuracy studies that have evaluated the ability of clinical features to identify concomitant fractures in patients with shoulder dislocation. All fractures were included except for Hill-Sachs lesions. Quality assessment was undertaken using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Data were pooled and meta-analysed by fitting univariate random effects and multi-level mixed effects logistic regression models. Results and Conclusion Eight studies reported data on 2,087 shoulder dislocations and 343 concomitant fractures. The prevalence of concomitant fracture was 17.5%. The most accurate clinical predictors were age >40 (LR+ 1.8 [95% CI 1.5–2.1]; LR- 0.4 [0.2–0.6]), female sex (LR+ 2.0 [1.6–2.4], LR- 0.7 [0.6–0.8]), first time dislocation (LR+ 1.7 [1.4–2.0]; LR-0.2 [0.1–0.5]), and presence of humeral ecchymosis (LR+ 3.0–5.7; LR- 0.8–1.1). The most important mechanisms of injury were: high-energy mechanism fall (LR+ 2.0–9.8), fall >1 flight of stairs (LR+ 3.8 [95% CI 0.6–13.1]; LR- 1.0 [95% CI 0.9–1.0]), and motor vehicle collision (LR+ 2.3 [0.5–4.0]; LR- 0.9 [0.9–1.0]). The Quebec Rule had a sensitivity of 92.2% (95% CI 54.6–99.2%) and specificity (33.3%, 23.1–45.3%) but the Fresno-Quebec rule maintained 100% sensitivity across three studies that included 564 shoulder dislocations and 98 fractures. In conclusion, the Fresno-Quebec Rule has undergone both internal and external validation and may now have a role in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".