The Role of Preoperative Antibiotics in Surgical Site Infection (SSI) Rate after Class I/Clean Gynaecologic Surgery at an Academic Safety Net Hospital
Bibliographic record
Abstract
OBJECTIVES: Examine whether preoperative antibiotics in class I/clean abdominal gynaecologic surgery decrease the incidence of surgical site infections (SSI). METHODS: Retrospective cohort study at academic safety net hospital of patients undergoing class I laparoscopic or open gynaecologic surgery between November 2013 and September 2017. Performance improvement initiative to administer preoperative antibiotics to all surgical patients starting July 2016. RESULTS: In total, 510 patients were included: 283 in the antibiotic group and 227 in the no-antibiotic group. PRIMARY OUTCOME: incidence of SSI. Baseline characteristics were similar between groups once balanced by propensity score method. In unweighted analysis, incidence of SSI decreased from 9.3% (21/227) in the no-antibiotics group to 4.9% (14/283) in antibiotics group, but this was not statistically significant (odds ratio (OR) 0.51 CI 0.25-1.03, P = 0.0598). Following of inverse probability of treatment weighting adjustments in weighted analysis, incidence of SSI was found to be significantly lower in patients who received antibiotics compared to patients who did not receive antibiotics across entry types (4.6% vs. 9.8%, OR 0.45; CI 0.22-0.90, P = 0.023). Weighted analysis demonstrated in the exploratory laparotomy group patients who received antibiotics had a lower incidence of SSI compared to patients who did not receive antibiotics (5.1% vs. 18.7%, OR 0.23; CI 0.08-0.68, P = 0.008). In the laparoscopy group, there was no difference between groups (4.4% vs. 5.4%, OR 0.81; CI 0.3-2.16, P = 0.675). CONCLUSIONS: There is limited literature on SSI prevention/preoperative antibiotic use in class I gynaecologic surgeries. This study demonstrates antibiotics in class I procedures decrease SSI rates, specifically in open procedures. There was a lack of demonstrated benefit in laparoscopy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".