Can Exposure Characterization Explain Concurrence or Discordance between Toxicology and Epidemiology?
Bibliographic record
Abstract
The importance of reliable exposure assessment, as a key component of the overall risk assessment process, has been well described for some considerable time. Yet, despite this widely accepted tenet, many studies conclude significant adverse health effects, with associated public policy implications, in the absence of adequate or, in some cases, even rudimentary, exposure quantification. Moreover, it appears that epidemiological studies in humans and toxicological studies in experimental animals may both suffer from inadequate exposure assessment. In this review, we discuss the nature and quality of the exposure assessment in both epidemiologic and toxicologic studies using examples from the pesticides and phthalate literature. Each type of study has its strengths and weaknesses in how exposure is assessed and often the strength of one is also a weakness. It would appear that insufficient or incomplete information about differences in exposure assessment could explain, at least in some cases, the differences in outcome between toxicological and epidemiological studies. Research efforts should focus on improving the feasibility of including biomonitoring in both animal and human studies to facilitate comparisons between animal and human models and improve exposure assessment in epidemiologic studies. Animal and human studies should measure the same biomarkers, where possible, to facilitate human health risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".