Implementation of Quebec Shoulder Dislocation Rule in Turkish Patient Population
Bibliographic record
Abstract
Aim: The aim of this study was to investigate whether the Quebec Shoulder Dislocation Rule (QSDR) is valid in Turkish patient population.Materials and Methods: Adult patients with an anterior shoulder dislocation were enrolled in this study. Patients with a severe head injury, multiple trauma, and drug or alcohol intoxication were excluded. All patients were classified according to the associated fracture status. Patients with fracture dislocation were classified as “case” group (group 1) and patients with isolated dislocation were included to the “control” group (group 2). Group 1 and 2 patients were statistically compared in terms of risk factors defined by QSDR. Diagnostic performance of this decision tool in predicting a clinically significant fracture was studied. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated.Results: A total of 248 patients were included in the study. Fracture dislocation was detected in 63 (25.40%) patients (group 1), and no complicated dislocation was detected in 185 (74.59%) patients (group 2). Statistically significant difference was found between groups 1 and 2 in terms of risk factors defined by the QSDR. Sensitivity, specificity, and positive and negative predictive values of the Quebec rule were detected as 100%, 33.51%, 33.87%, and 98.41%, respectively.Conclusion: Our study suggests that QSDR is a useful decision tool to estimate which patient has fracture dislocation of the shoulder in Turkish population. We think that if a shoulder dislocation patient is “Quebec–,” pre-reduction X-ray could be safely omitted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".