O2E.4 Evaluation of exposure assessment methods in epidemiological studies: the welding example
Bibliographic record
Abstract
Exposure assessment quality is a fundamental consideration in the design and evaluation of observational studies. High quality exposure assessment is particularly relevant for outcomes with long latency, such as cancer, where detailed information on past exposures are often missing and must therefore be estimated. For the IARC Monograph on welding, the exposure group provided an overview of assessment methods used in the key epidemiological studies. Strengths and weaknesses of each study were assessed, along with their potential effects on interpretation of risk estimates. For the association between lung cancer and welding fume exposure, 9 cohort and 10 case-control studies were reviewed. For ocular melanoma and ultraviolet radiation (UVR) from welding, 7 case-control studies were reviewed. Quality criteria were: full occupational histories, and standardized, blinded and quantitative exposure assessment. Additional criteria for lung cancer: specifically assessing welding fumes and using information on welding tasks. For ocular melanoma: assessing artificial and solar radiation separately, taking into account eye burns, eye protection and welding type. Exposure assessment of welding fumes by applying a ‘welding-exposure matrix’ (n=2) or welding-specific questionnaires (n=3) were considered highest quality, followed by case-by-case expert assessment (n=5) or general job-exposure matrices (JEMs, n=4). Job title alone was considered less informative (n=5). For exposure to UVR, JEMs were most informative (n=2), followed by self-reported eye burns and self-reported exposure from specific welding types (n=2), although caution is advised regarding recall bias. Assessing welding fume exposure or ever exposure to welding arcs as proxy for UVR was considered less informative. For both exposures, ever versus never welder, or assessments based on data collected from proxies, were considered least informative. The overall evaluation was that there is sufficient evidence in humans for the carcinogenicity of welding fumes and ultraviolet radiation from welding.
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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 | 0.208 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".