Changes in proportions of Cesarean section before and during the <scp>COVID</scp> ‐19 pandemic in Japan
Bibliographic record
Abstract
AIM: After the coronavirus disease 2019 (COVID-19) pandemic, Cesarean sections for COVID-19-positive cases were performed to reduce delivery time and thus control infection. This may have increased the proportion of Cesarean sections and affected many pregnant women in Japan; though this expected trend has not yet been quantified. This study examined changes in the proportions of Cesarean sections in Japan before and during the pandemic. METHODS: This study retrospective observational study used the National Database of Health Insurance Claims and Specific Health Checkups of Japan and Vital Statistics from the National Statistical Surveys from April 2018 to October 2022. We compared proportions of Cesarean sections (total Cesarean sections/total live births) in Japan before and during the pandemic and by the COVID-19 pandemic phase: pre-COVID-19 (April 2018 to December 2019), Wave 1 (January to May 2020), Wave 2 (June to October 2020), Wave 3 (November 2020 to February 2021), Wave 4 (March to June 2021), Wave 5 (July to December 2021), Wave 6 (January to June 2022), and Wave 7 (July to October 2022). RESULTS: The proportion of Cesarean sections in Japan was 20.27% (317 241/1 564 912) before the pandemic and increased to 21.19% (486 172/2 294 488) during the pandemic. The highest proportion was in Wave 6 (22.14%), dropping to 21.27% in Wave 7. CONCLUSION: The overall proportion of Cesarean sections increased by 0.92% point during the COVID-19 pandemic in Japan, possibly due in part to infection control measures. Verification and preparation are necessary to respond to future pandemics.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".