Cohort profile: Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS)
Bibliographic record
Abstract
Purpose The Cook Stove Pregnancy Cohort Study (CSPCS) was designed to assess the effects of biomass fuel use on household air pollution (HAP) as well as the effects of HAP (fine particulate matter, PM 2.5 ) on birth outcomes and acute lower respiratory infection (ALRI) among infants in Bangladesh. Participants We recruited 903 women within 18 weeks of pregnancy from rural and semiurban areas of Bangladesh between November 2016 and March 2017. All women and their infants (N=831 pairs) were followed until 12 months after delivery and a subset have undergone respiratory and gut microbiota analysis. Methods Questionnaires were administered to collect detailed sociodemographic, medical, nutritional and behavioural information on the mother–child dyads. Anthropometric measurements and biological samples were also collected, as well as household PM 2.5 concentrations. Findings to date Published work in this cohort showed detrimental effects of biomass fuel and health inequity on birth outcomes. Current analysis indicates high levels of household PM 2.5 being associated with cooking fuel type and infant ALRI. Lastly, we identified distinct gut and respiratory microbial communities at 6 months of age. Future plans This study provides an economical yet effective framework to conduct pregnancy cohort studies determining the health effects of adverse environmental exposures in low-resource countries. Future analyses in this cohort include assessing the effect of indoor PM 2.5 levels on (1) physical growth, (2) neurodevelopment, (3) age of first incidence and frequency of ALRI in infants and (4) the development of the respiratory and gut microbiome. Additional support has allowed us to investigate the effect of in utero exposure to metals on infant neurodevelopment in the first year of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".