Perinatal outcomes in a South Asian setting with high rates of low birth weight
Bibliographic record
Abstract
BACKGROUND: It is unclear whether the high rates of low birth weight in South Asia are due to poor fetal growth or short pregnancy duration. Also, it is not known whether the traditional focus on preventing low birth weight has been successful. We addressed these and related issues by studying births in Kaniyambadi, South India, with births from Nova Scotia, Canada serving as a reference. METHODS: Population-based data for 1986 to 2005 were obtained from the birth database of the Community Health and Development program in Kaniyambadi and from the Nova Scotia Atlee Perinatal Database. Menstrual dates were used to obtain comparable information on gestational age. Small-for-gestational age (SGA) live births were identified using both a recent Canadian and an older Indian fetal growth standard. RESULTS: The low birth weight and preterm birth rates were 17.0% versus 5.5% and 12.3% versus 6.9% in Kaniyambadi and Nova Scotia, respectively. SGA rates were 46.9% in Kaniyambadi and 7.5% in Nova Scotia when the Canadian fetal growth standard was used to define SGA and 6.7% in Kaniyambadi and < 1% in Nova Scotia when the Indian standard was used. In Kaniyambadi, low birth weight, preterm birth and perinatal mortality rates did not decrease between 1990 and 2005. SGA rates in Kaniyambadi declined significantly when SGA was based on the Indian standard but not when it was based on the Canadian standard. Maternal mortality rates fell by 85% (95% confidence interval 57% to 95%) in Kaniyambadi between 1986-90 and 2001-05. Perinatal mortality rates were 11.7 and 2.6 per 1,000 total births and cesarean delivery rates were 6.0% and 20.9% among live births >or= 2,500 g in Kaniyambadi and Nova Scotia, respectively. CONCLUSION: High rates of fetal growth restriction and relatively high rates of preterm birth are responsible for the high rates of low birth weight in South Asia. Increased emphasis is required on health services that address the morbidity and mortality in all birth weight categories.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".