Printed in U.SA. Self-assessed Versus Expert-assessed Occupational Exposures
Bibliographic record
Abstract
While self-response to a checklist of substances may be a convenient and inexpensive method for obtaining information on occupational exposure, the validity of such information has not been evaluated. The objective of this report is to provide some evidence concerning validity of self-reported occupational exposures. In the context of a large case-control study, it was possible to compare self-reports with expert assessment, in which a team of industrial hygienists and chemists examined each job history individually and decided on likelihood of exposure. The subjects were 1,910 males who had participated in a population-based case-control study of cancer and occupational exposures conducted in Montreal, Canada, between 1979 and 1985. For each of 11 substances, the two methods of exposure assessment were compared by means of a kappa statistic and by computing the sensitivity and specificity of self-assessment against expert assessment. Kappa values ranged from 0.33 to 0.64. Compared with the expert assessment, specificities of the self-assessment were generally high (0.83-0.97, with a median of 0.90), but sensitivities were low (0.39-0.91, with a median of 0.61). The authors conclude that self-reports of occupational exposure are not sufficiently accurate to warrant their sole use in most community-based studies. Am J Epidemiol 1996;144:521-7. epidemiologic methods; occupational exposure Occupational risk factors for cancer can be investi-
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.663 | 0.429 |
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".