Birth Cohort Consortium of Asia
Bibliographic record
Abstract
BACKGROUND: The environmental health of children is one of the great global health concerns. Exposures in utero and throughout development can have major consequences on later health. However, environmental risks or disease burdens vary from region to region. Birth cohort studies are ideal for investigating different environmental risks. METHODS: The principal investigators of three birth cohorts in Asia including the Taiwan Birth Panel Study (TBPS), the Mothers and Children's Environmental Health Study (MOCEH), and the Hokkaido Study on Environment and Children' Health (Hokkaido Study) coestablished the Birth Cohort Consortium of Asia (BiCCA) in 2011. Through a series of five PI meetings, the enrolment criteria, aim of the consortium, and a first-phase inventory were confirmed. RESULTS: To date, 23 birth cohorts have been established in 10 Asian countries, consisting of approximately 70,000 study subjects in the BiCCA. This article provides the study framework, environmental exposure and health outcome assessments, as well as maternal and infant characteristics of the participating cohorts. CONCLUSIONS: The BiCCA provides a unique and reliable source of birth cohort information in Asian countries. Further scientific cooperation is ongoing to identify specific regional environmental threats and improve the health of children in Asia.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".