Open Ankle Fractures: What Predicts Infection? A Multicenter Study
Bibliographic record
Abstract
Objective: To identify the patient, injury, and treatment factors associated with an acute infection during the treatment of open ankle fractures in a large multicenter retrospective review. To evaluate the effect of infectious complications on the rates of nonunion, malunion, and loss of reduction. Design: Multicenter retrospective review. Setting: Sixteen trauma centers. Patients: One thousand and 3 consecutive skeletally mature patients (514 men and 489 women) with open ankle fractures. Main Outcome Measures: Fracture-related infection (FRI) in open ankle fractures. Results: The charts of 1003 consecutive patients were reviewed, and 712 patients (357 women and 355 men) had at least 12 weeks of clinical follow-up. Their average age was 50 years (range 16–96), and average BMI was 31; they sustained OTA/AO types 44A (12%), 44B (58%), and 44C (30%) open ankle fractures. The rate FRI rate was 15%. A multivariable regression analysis identified male sex, diabetes, smoking, immunosuppressant use, time to wound closure, and wound location as independent risk factors for infection. There were 77 cases of malunion, nonunion, loss of reduction, and/or implant failure; FRI was associated with higher rates of these complications (P = 0.01). Conclusions: Several patient, injury, and surgical factors were associated with FRI in the treatment of open ankle fractures. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".