Critical reviews of exposure assessment in carcinogenic hazard identification: the IARC<i>Monographs</i>experience
Bibliographic record
Abstract
Objectives To summarise the rationale, workflow and recommendations for the conduct of exposure assessment critiques in key human studies evaluated for International Agency for Research on Cancer (IARC) Monographs on the Identification of Carcinogenic Hazards. Methods Approaches to evaluating exposure assessment quality in human cancer and mechanistic studies were reviewed according to the precepts outlined in the IARC Monographs Preamble, using two agents as case studies. Exposure assessment ‘domains’, that is, salient aspects of exposure assessment for the agent under evaluation, were selected for review across the key human studies. Results The case studies of night shift work (volume 124) and 1,1,1-trichloroethane (volume 130) used a common approach, tailored to the agents’ specific exposure scenarios, to evaluate exposure assessment quality. Based on the experiences of IARC Working Groups to date, the implementation of exposure assessment critique requires the need for agent-specific knowledge, consideration of the validity of time-varying exposure metrics related to duration and intensity, and transparent, concise reviews that prioritise the most important strengths and limitations of exposure assessment methods used in human studies. Conclusions Exposure assessment has not historically been a fully appreciated component for evaluating the quality of epidemiological studies in cancer hazard identification. Exposure assessment critique in key human cancer and mechanistic studies is now an integral part of IARC Monographs evaluations and its conduct will continue to evolve as new agents are evaluated. The approaches identified here should be considered as a potential framework by others when evaluating the exposure assessment component of epidemiological studies for systematic reviews.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".