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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".