Characterizing the effect of endocrine disruptors on human health: The role of epidemiological cohorts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it