Rationale and design of South Asian Birth Cohort (START): a Canada-India collaborative study
Bibliographic record
Abstract
BACKGROUND: People who originate from the Indian subcontinent (South Asians) suffer among the highest rates of type 2 diabetes in the world. Prior evidence suggests that metabolic risk factors develop early in life and are influenced by maternal and paternal behaviors, the intrauterine environment, and genetic factors. The South Asian Birth Cohort Study (START) will investigate the environmental and genetic basis of adiposity among 750 South Asian offspring recruited from highly divergent environments, namely, rural and urban India and urban Canada. METHODS: Detailed information on health behaviors including diet and physical activity, and blood samples for metabolic parameters and DNA are collected from pregnant women of South Asian ancestry who are free of significant chronic disease. They also undergo a provocative test to diagnose impaired glucose tolerance and gestational diabetes. At delivery, cord blood and newborn anthropometric indices (i.e. birth weight, length, head circumference and skin fold thickness) are collected. The mother and growing offspring are followed prospectively and information on the growth trajectory, adiposity and health behaviors will be collected annually up to age 3 years. Our aim is to recruit a minimum of 750 mother-infant pairs equally divided between three divergent environments: rural India, urban India, and Canada. SUMMARY: The START cohort will increase our understanding of the environmental and genetic determinants of adiposity and related metabolic abnormalities among South Asians living in India and Canada.
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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 | 0.047 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".