Impact of relaxation of the one‐child policy on maternal mortality in Guangzhou, China
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of the one-child policy in China on maternal mortality. METHODS: The present retrospective study included maternal death data from Guangdong, China, from January 1, 2006, to December 31, 2015; data from 2013 were excluded because they were not available. Maternal deaths were divided into legal and illegal pregnancies based on adherence to the one-child policy. The maternal mortality ratio (MMR) was compared between the groups, temporal trends in the MMR were examined, and comparisons were made of the causes of death and access to maternity care. RESULTS: The final analysis included 847 520 live deliveries and 383 maternal deaths. The MMR among legal pregnancies declined moderately from 18.5 deaths per 100 000 live deliveries in 2006 to 12.2 deaths per 100 000 live deliveries in 2015 (P=0.029), whereas the MMR among illegal pregnancies declined dramatically from 1268.4 deaths per 100 000 live deliveries to 177.5 deaths per 100 000 live deliveries (P<0.001). The proportion of avoidable maternal deaths decreased and access to quality maternity care improved among illegal pregnancies during the study period. CONCLUSIONS: Maternal mortality among illegal pregnancies declined with relaxation of the one-child policy in China.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".