Characterizing the effect of endocrine disruptors on human health: The role of epidemiological cohorts
Bibliographic record
Abstract
Research on endocrine disruptors (EDs) developed from numerous disciplines. In this concert of disciplines, epidemiology is central to inform on the relevance for humans of mechanisms and dose-response functions identified in animals, to characterize the health impact (number of attributable disease cases), the cost associated with ED exposure, and the efficiency of the measures taken to limit exposure. Here, we present epidemiological tools to draw valid inference regarding effects of potential EDs. Epidemiology is generally observational, requiring care to control confounding bias. Many potential EDs have a short biological half-life; approaches relying on repeated biospecimens sampling allow limiting exposure misclassification and the resulting bias. For non-persistent compounds, couple-child cohorts are a central study design. Cohorts can now rely on molecular biology approaches to characterize exposures and intermediate pathways, which corresponds to the advent of molecular epidemiology and allows stronger interactions between epidemiology, toxicology, and molecular epidemiology to characterize the health effects of EDs.
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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 | 0.052 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".