Rate of and Risk Factors for Intermediate-Term Reoperation After Ankle Fracture Fixation: A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Establish baseline rates of and risk factors for reoperation within 1 or 2 years of ankle open reduction internal fixation (ORIF). DESIGN: Retrospective, population-based cohort study. SETTING: Two hundred two hospitals in Ontario, Canada (approximate population 13.6 million in 2014). PATIENTS/PARTICIPANTS: Forty five thousand four hundred forty-four patients who underwent ankle ORIF performed by 710 different surgeons between January 1, 1994, and December 31, 2011. MAIN OUTCOME MEASUREMENTS: Intermediate-term reoperation because of isolated implant removal, repeat ORIF, irrigation and debridement (I&D) for infection, or amputation. Multivariable logistic regression related potential prognostic factors (patient, provider, and injury) to reoperation. RESULTS: There were 8906 patients who underwent at least one subsequent operation (19.6%). The most common procedure was isolated implant removal (18.1%); odds of removal being higher for females [odds ratio (OR), 1.53; 95% confidence interval (CI), 1.45-1.62; P < 0.001]. N = 674 patients (1.5%) underwent reoperation for another reason. The odds of repeat ORIF and I&D infection were greater for open fractures (OR 2.17; 95% CI, 1.22-3.86; P = 0.008 and OR 3.12; 95% CI, 1.94-5.03; P < 0.001). Odds of amputation was highest for diabetics (OR 7.42; 95% CI, 3.73-14.86; P < 0.001). CONCLUSIONS: Isolated implant removal accounts for the vast majority of intermediate-term reoperations after ankle ORIF. Reoperation for other reasons (repeat ORIF, I&D, or amputation) was extremely rare, even among the highest risk patients. Concerns regarding reoperation for these reasons should not preclude operative treatment in any patient, provider, or injury group we considered. LEVEL OF EVIDENCE: Prognostic Level II. 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".