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 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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".