How the COVID-19 pandemic changed postoperative infections in urology wards: A retrospective cohort study from two urology departments
Bibliographic record
Abstract
INTRODUCTION: We aimed to compare the rate of postoperative infection and drug-resistant organism (DRO) before and during the COVID-19 pandemic in urology departments. METHODS: A retrospective cohort study was carried out. Data from all elective surgical procedures carried out in two urology departments between April and June 2018 and the homologous period in 2020 were collected. Main outcomes were the number of postoperative infections during the pandemic and the number of DROs. Sample size was calculated based on a 50% relative reduction of infections during the pandemic. Variables were compared by Chi-squared test, and multivariable logistic regression was used to estimate predictors. RESULTS: A total of 698 patients undergoing elective surgery were included. The postoperative infection rate during the pre-pandemic period was of 14.1% compared to 12.1% during the pandemic (p=0.494). DROs were lower during the pandemic (92.3% vs. 52.4%, p=0.002). The pandemic period was the main predictor for reduced multidrug-resistant isolates, with an odds ratio of 0.10 (p=0.010, 95% confidence interval 0.016-0.57). CONCLUSIONS: Postoperative infection rates were not significantly reduced during the COVID-19 pandemic, despite the adoption of enhanced infection preventive measures. There was, however, a decrease in the rate of DROs during this period, suggesting a secondary benefit to enhanced infection prevention practices adopted during the COVID-19 era.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".