Endocrine disruptors: Challenges and future directions in epidemiologic research
Bibliographic record
Abstract
Public concern about the impact of endocrine disrupting chemicals (EDCs) on both humans and the environment is growing steadily. Epidemiologic research provides key information towards our understanding of the relationship between environmental exposures like EDCs and human health outcomes. Intended for researchers in disciplines complementary to epidemiology, this paper highlights the importance and challenges of epidemiologic research in order to present the key elements pertaining to the design and interpretation of an epidemiologic study on EDCs. The conduct of observational studies on EDCs derives from a thoughtful research question, which will help determine the subsequent methodological choices surrounding the careful selection of the study population (including the comparison group), the adequate ascertainment of exposure(s) and outcome(s) of interest, and the application of methodological and statistical concepts more specific to epidemiology. The interpretation of epidemiologic results may be arduous due to the latency occurring between EDC exposure and certain outcome(s), the complexity in capturing EDC exposure(s), and traditional methodological and statistical issues that also deserve consideration (e.g., confounding, effect modification, non-monotonic responses). Moving forward, we strongly advocate for an integrative approach of expertise in the fields of epidemiology, exposure science, risk assessment and toxicology to adequately study the health risks associated with EDCs while tackling their challenges.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".