A Reevaluation of the Risk of Infection Based on Time to Debridement in Open Fractures
Bibliographic record
Abstract
BACKGROUND: Open fractures are one of the leading causes of disability worldwide. The threshold time to debridement that reduces the infection rate is unclear. METHODS: We searched all available databases to identify observational studies and randomized trials related to open fracture care. We then conducted an extensive meta-analysis of the observational studies, using raw and adjusted estimates, to determine if there was an association between the timing of initial debridement and infection. RESULTS: We identified 84 studies (18,239 patients) for the primary analysis. In unadjusted analyses comparing various "late" time thresholds for debridement versus "early" thresholds, there was an association between timing of debridement and surgical site infection (odds ratio [OR] = 1.29, 95% confidence interval [CI] = 1.11 to 1.49, p < 0.001, I2 = 30%, 84 studies, n = 18,239). For debridement performed between 12 and 24 hours versus earlier than 12 hours, the OR was higher in tibial fractures (OR = 1.37, 95% CI = 1.00 to 1.87, p = 0.05, I2 = 19%, 12 studies, n = 2,065), and even more so in Gustilo type-IIIB tibial fractures (OR = 1.46, 95% CI = 1.13 to 1.89, p = 0.004, I2 = 23%, 12 studies, n = 1,255). An analysis of Gustilo type-III fractures showed a progressive increase in the risk of infection with time. Critical time thresholds included 12 hours (OR = 1.51, 95% CI = 1.28 to 1.78, p < 0.001, I2 = 0%, 16 studies, n = 3,502) and 24 hours (OR = 2.17, 95% CI = 1.73 to 2.72, p < 0.001, I2 = 0%, 29 studies, n = 5,214). CONCLUSIONS: High-grade open fractures demonstrated an increased risk of infection with progressive delay to debridement. LEVEL OF EVIDENCE: Prognostic Level IV. See Instruction for Authors for a complete description of the 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 | 0.081 | 0.215 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.025 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".