Use of Femoral Shaft Fracture Classification for Predicting the Risk of Associated Injuries
Bibliographic record
Abstract
OBJECTIVES: To investigate the hypothesis that specific fracture patterns in patients with femoral shaft fractures can predict the likelihood of associated injuries. DESIGN: Retrospective cohort study. SETTING: Level I trauma center. PATIENTS/PARTICIPANTS: Consecutive patients treated because of a traumatic diaphyseal femoral fracture. MAIN OUTCOME MEASUREMENT: We studied the association between the Orthopaedic Trauma Association (OTA) fracture classification (derived from initial radiographs) and concomitant injuries of the head, spine, chest, abdomen, and pelvis with a severity of two or more points according to the Abbreviated Injury Scale by logistic regression analysis. RESULTS: One hundred forty-three of 203 patients (80 men, 63 women; mean age 54 ± 26 years) met the inclusion criteria. All patients had unilateral diaphyseal fractures, 64 OTA 32.A (45%), 46 OTA 32.B (32%), and 33 OTA 32.C (23%). In addition, 134 associated injuries were identified in 52 patients. Increasing fracture severity, as expressed by the OTA classification (ie, A, B, C), was significantly associated with a higher likelihood of thoracic (odds ratio [OR], 5.89; 95% confidence interval [CI], 2.59-13.40), pelvic (OR, 4.55; 95% CI, 2.01-10.28), upper (OR, 2.38; 95% CI, 1.27-4.48), and lower extremity injuries (OR, 3.12; 95% CI, 1.78-5.46). Fracture severity explained between 70% and 86% of the probability of having accompanying injuries. CONCLUSION: Radiographic grading of the severity of a femoral shaft fracture may signal the presence of accompanying injuries and should contribute to the clinical decision-making process in severe trauma.
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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 | 0.001 | 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 | 0.000 |
| 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, 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".