Tales from Two Cohots
Bibliographic record
Abstract
Background/Aim: Developmental neurotoxicity is a global health concern. Neurotoxicants easily cross the placenta and fetal blood brain barrier, and their constant presence in maternal blood ensures that the fetus is exposed during critical periods of brain development. Exposure to neurotoxicants has been shown to be associated with children's neurobehavioural outcomes, which in turn has economic, social and health consequences. The aim of this presentation is to describe two large longitudinal pregnancy cohorts investigating associations between prenatal and childhood exposures to neurotoxicants and children's neurodeveloopmental outcomes in a high (Canada) and a low income country (Tanzania).Method and Results: The Alberta Pregnancy Outcomes and Nutrition (APrON) cohort (2189 mothers) was recruited in Alberta, Canada. Data on prenatal exposure to endocrine disruptors (i.e., BPA, BPS, phthalates), heavy metals (i.e., methyl mercury, lead, arsenic, manganese) and perfluorooctane sulfonate (PFOS) during the second trimester of pregnancy has been collected for 563 children. In addition, we have collected biosamples at 3-4 years of age to assess childhood exposure levels. Children’s neurodevelopment and behaviour at 2, 3-4 and 5-6 years is being assessed across multiple domains using standardized measures. The Mining and Health cohort (N = 1056) was recruited in Geita and Magu Districts, Tanzania where artisanal and small scale gold mining is a significant industry. In this cohort, data is being collected on prenatal exposure to mercury, arsenic and other heavy metals, pregnancy and birth outcomes, and early childhood health and development.Conclusions: These two pregnancy cohorts, one from a high income country and the other from a low income country, are providing much needed data on known and emerging neurotoxicants and their long term effects on children’s health, neurodevelopment and behaviour.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.008 |
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".