Performance of the Fresno-Quebec Rule in identifying patients with concomitant fractures not requiring a radiograph before shoulder dislocation reduction: a multicenter retrospective cohort study
Bibliographic record
Abstract
BACKGROUND AND IMPORTANCE: Although shoulder dislocation diagnosis is often solely based on clinical examination, physicians may order a radiograph to rule out a concomitant shoulder fracture before performing reduction. The Fresno-Québec decision rule aims to identify patients requiring a radiograph before reduction to avoid unnecessary systematic imaging. However, this novel approach needs further validation. OBJECTIVE: To evaluate the performance of the Fresno-Québec rule in identifying patients who do not require a prereduction radiograph and assess the variables associated with a clinically significant fracture. DESIGN, SETTINGS, AND PARTICIPANTS: A multicenter, retrospective cohort study from 2015 to 2021. Data were extracted from three ED university-affiliated tertiary-care centers. Patients aged ≥18 years with a final diagnosis of anterior glenohumeral dislocation were included. OUTCOMES MEASURE AND ANALYSIS: Accuracy metrics [sensitivity (Se), specificity (Sp), positive (PPV), negative predictive value (NPV), positive likelihood ratio (PLR) and negative likelihood ratio (NLR)] of the Fresno-Québec rule were measured. Multivariable logistic regression model was used to identify variables associated with the presence of a concomitant clinically significant fracture. MAIN RESULTS: A total of 2129 patients were included, among whom 9.7% had a concomitant fracture. The performance metrics of the Fresno-Québec rule were as follows: Se 0.96 95% confidence interval (0.92-0.98), Sp 0.36 (0.34-0.38), PPV 0.14 (0.12-0.16), NPV 0.99 (0.98-0.99), PLR 1.49 (1.42-1.55) and NLR 0.12 (0.06-0.23). A total of 678 radiographs could have been avoided, corresponding to a reduction of 35.2%. Age ≥40 years, first dislocation episode [odds ratio (OR) = 3.18 (1.95-5.38); P < 0.001], the following mechanisms: road collision [OR = 6.26 (2.65-16.1)], low-level fall [OR = 3.49 (1.66-8.28)], high-level fall [OR = 3.95 (1.62-10.4)], and seizure/electric shock [OR = 10.6 (4.09-29.2)] were associated with the presence of a concomitant fracture. CONCLUSION: In this study, the Fresno-Québec rule has excellent Se in identifying concomitant clinically significant fractures in patients with an anterior glenohumeral dislocation. The use of this clinical decision rule may be associated with a reduction of approximately a third of unnecessary prereduction radiographs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".