Screening tool for identification of hip fractures in the prehospital setting
Bibliographic record
Abstract
Abstract Objectives: This study aims to develop a screening tool that will help first responders identify patients with proximal femur fractures, commonly referred to as hip fractures, on site and direct these patients to hospitals with orthopaedic surgery services. Study Design: Prospective survey. Methods: Literature and expert opinion defined parameters for the Collingwood Hip Fracture Rule (CHFR) which predict a patient's likelihood of hip fracture. The study population included adults presenting to Collingwood General and Marine Hospital with lower extremity injuries between December 1, 2019 and March 10, 2020. Excluded patients had previous hip replacement, previous hip fracture on the side of the injury, or a high energy mechanism of injury. Patients were assessed with the CHFR before receiving x-ray imaging. The parameters were scored based on their predictive powers and analyzed by a receiver operating characteristic curve. Results: The study included 101 patients (mean age 66.3 years), and 25.7% had a hip fracture confirmed on imaging. The sensitivity, specificity, positive predictive value, and negative predictive value helped score each parameter. Factors receiving 1 point are: age 65 to 79 years, female, mechanical fall, unable to weight-bear, knee pain. Factors receiving 2 points are: bruising at greater trochanter, age >80 years. Factors receiving 3 points are: pain with hip rotation, leg shortened and externally rotated. Score is the summation of all the factors’ points. The receiver operating characteristic curve (0.953; P value < .0001) demonstrated scores of 7 had sensitivity:specificity of 84.6%:94.7%. Conclusion: The CHFR screening tool score of 7 can be used by first responders in the prehospital setting to identify patients who sustain a hip fracture and make appropriate triage decisions. This will improve patient outcomes and decrease institutional costs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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, 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".