Trends and predictors of cesarean birth in Singapore, 2005‐2014: A population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: Rates of cesarean birth have continued to rise in many high-income countries. We examined the temporal trends and predictors of cesarean birth in Singapore. METHODS: Linked hospitalization and Birth Registry data were used to examine all live births to Singaporean citizens and permanent residents between January 1, 2005 and December 31, 2014 (n = 342 932 births). We calculated cesarean rates and age-adjusted average annual percent change (AAPC) in those rates and used sequential multivariable regression modeling to assess the contribution of changes in predictors to the change in cesarean rates over time. RESULTS: The overall cesarean rate in Singapore rose from 32.2% in 2005 to 37.4% in 2014. Among singleton, cephalic, term pregnancies, the two major predictions of cesarean were nulliparity and previous cesarean, each accounting for just over one-third of all cesareans. Higher AAPC was observed in nulliparous women of Indian ethnicity (0.74% [95% confidence interval 0.68-0.80]) compared with Chinese (0.62% [0.60-0.65]) or Malay women (0.63% [0.59-0.68]), and in women who delivered in private hospitals (0.62% [0.60-0.64]) compared with those delivered under subsidized care in public hospitals (0.58% [0.52-0.63]). Parity and education had the largest influences on cesarean birth trend (attenuation of AAPC from 0.62% [0.59-0.66] to 0.39% [0.38-0.40] after adjustment). CONCLUSION: Cesarean birth has continued to rise at a steady rate in Singapore. Strategies to curb this temporal increase include avoidance of medically unnecessary primary cesarean and attempts at trial of labor and vaginal delivery among women with a history of prior cesarean.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".