Limited Impact of COVID-19 on Pre-existing Trends in Postpartum Infection
Bibliographic record
Abstract
OBJECTIVES: To assess whether postpartum infection rates decreased among hospital deliveries during the pandemic. METHODS: We designed a pre-post pandemic comparison study of postpartum infection rates among hospital births in Québec, Canada between 2017 and 2022. We calculated postpartum infection rates per 10 000 deliveries and compared the prepandemic and pandemic periods using risk ratios (RRs) and 95% CIs from log-binomial multivariable regression models. We stratified the analysis by mode of delivery and adjusted the models for maternal age, parity, comorbidity, and socioeconomic deprivation. We used autoregressive interrupted time series analysis to verify whether the pandemic impacted pre-existing trends in postpartum infection rates. RESULTS: Postpartum infections were less frequent during the pandemic. Among patients who underwent cesarean deliveries, there was a 30% reduction in the risk of postpartum infection during the pandemic compared with the prepandemic period (RR 0.70; 95% CI 0.62-0.79, P < 0.001). Among patients who underwent vaginal deliveries, there was a 12% reduction in risk (RR 0.88; 95% CI 0.81-0.97, P= 0.008). However, interrupted time series analysis indicated that the reduction in postpartum infections began as early as 2010 and that these pre-existing trends persisted during the pandemic. CONCLUSIONS: Infection rates have decreased over time, especially among cesarean deliveries. The pandemic did not significantly alter the long-term trend in decreasing postpartum infection rates.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".