Trends and predictors of cesarean birth in Singapore, 2005‐2014: A population‐based cohort study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it