Risk factors for surgical site infection following cesarean delivery: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The rate of cesarean delivery is increasing in North America. Surgical site infection following this operation can make it difficult to recover, care for a baby and return home. We aimed to determine the incidence of surgical site infection to 30 days following cesarean delivery, associated risk factors and whether risk factors differed for predischarge versus postdischarge infection. METHODS: We identified a retrospective cohort in Nova Scotia by linking the provincial perinatal database to hospital admissions and physician billings databases to follow women for 30 days after they had given birth by cesarean delivery between Jan. 1, 1997 and Dec. 31, 2012. Logistic regression with generalized estimating equations was used to determine risk factors for infection. RESULTS: A total of 25 123 women had 33 991 cesarean deliveries over the study period. Of the 25 123, 923 had surgical site infections, giving an incidence rate of 2.7% (95% CI 2.54%-2.89%); the incidence decreased over time. Risk factors for infection (adjusted odds ratios ≥ 1.5) were prepregnancy weight 87.0 kg or more, gaining 30.0 kg or more during pregnancy, chorioamnionitis, maternal blood transfusion, anticoagulation therapy, alcohol or drug abuse, second stage of labour before surgery, delivery in 1997-2000 and delivery in a hospital performing 130-1249 cesarean deliveries annually. Women who gave birth earlier in the study period, those who gave birth in a hospital with 130-949 cesarean deliveries per year and those with more than 1 fetus were at a significantly higher risk for surgical site infection before discharge; women who smoked were at significantly higher risk for surgical site infection after discharge. INTERPRETATION: Most risk factors are known before delivery, and some are potentially modifiable. Although the incidence of surgical site infection decreased over time, targeted clinical and infection prevention and control interventions could further reduce the burden of illness associated with this health-care-related infection.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".