Assessing Occupational Exposure to Chemicals in an International Epidemiological Study of Brain Tumours
Bibliographic record
Abstract
The INTEROCC project is a multi-centre case-control study investigating the risk of developing brain cancer due to occupational chemical and electromagnetic field exposures. To estimate chemical exposures, the Finnish Job Exposure Matrix (FINJEM) was modified to improve its performance in the INTEROCC study and to address some of its limitations, resulting in the development of the INTEROCC JEM. An international team of occupational hygienists developed a crosswalk between the Finnish occupational codes used in FINJEM and the International Standard Classification of Occupations 1968 (ISCO68). For ISCO68 codes linked to multiple Finnish codes, weighted means of the exposure estimates were calculated. Similarly, multiple ISCO68 codes linked to a single Finnish code with evidence of heterogeneous exposure were refined. One of the key time periods in FINJEM (1960-1984) was split into two periods (1960-1974 and 1975-1984). Benzene exposure estimates in early periods were modified upwards. The internal consistency of hydrocarbon exposures and exposures to engine exhaust fumes was improved. Finally, exposure to polycyclic aromatic hydrocarbon and benzo(a)pyrene was modified to include the contribution from second-hand smoke. The crosswalk ensured that the FINJEM exposure estimates could be applied to the INTEROCC study subjects. The modifications generally resulted in an increased prevalence of exposure to chemical agents. This increased prevalence of exposure was not restricted to the lowest categories of cumulative exposure, but was seen across all levels for some agents. Although this work has produced a JEM with important improvements compared to FINJEM, further improvements are possible with the expansion of agents and additional external data.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".