Time to Surgical Closure of Complex Infectious Wounds: A Single-center Retrospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Surgical management of NSTIs can result in complex wounds, and closure of these wounds is often difficult or complicated. Although surgical factors influencing mortality and LOS have been well described, little is known about patient, wound, and surgical factors associated with time to closure. OBJECTIVE: The purpose of this study is to identify patient, wound, and surgical factors that may influence time to closure of NSTIs. MATERIALS AND METHODS: The records of patients who presented to a tertiary care center over an 11-year period (2007-2017) with an NSTI requiring surgical closure were retrospectively reviewed. RESULTS: Forty-seven patients met the inclusion criteria. The average time to closure was 31.1 days, with an average of 4.8 procedures. Most patients were middle aged (mean, 50.3 years; range, 20-81 years), immunocompetent, and nondiabetic upon admission. Closure was achieved mainly with autograft. The percent TBSA was described in 19 cases (40%). There was no association between substance use (alcohol, smoking, or other), anticoagulant medication use, or medical comorbidities and time to closure. On multivariable analysis, flap closure (P =.02) and increased number of surgical procedures (P =.003)-the latter reflecting the need for an increased number of debridements-were associated with increased time to closure. CONCLUSIONS: The data in this study suggest that use of local flaps for wound closure and increased number of surgical procedures (particularly debridements) may be predictors of time to closure in patients with an NSTI.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".