Occupational solvent exposure and risk of glioma in the INTEROCC study
Bibliographic record
Abstract
BACKGROUND: The aetiology of glioma remains largely unknown. Occupational solvent exposure has been suggested as a putative cause of glioma, but past studies have been inconsistent. We examined the association between a range of solvents and glioma risk within the INTEROCC project, a study of brain tumours and occupational exposures based on data from seven national case-control studies conducted in the framework of the INTERPHONE study. We also investigated associations according to tumour grade. METHODS: Data from the seven countries were standardised and then combined into one aggregate data set. Pooled odds ratios (ORs) were estimated for adjusted models that included sex, age, country-region of residence and level of educational attainment. Exposures to any solvent or 11 specific solvents or subgroups were assessed using a modified version of the FINJEM job exposure matrix (JEM) specifically developed for the study, called INTEROCC-JEM. RESULTS: Analysis included 2000 glioma cases and 5565 controls. For glioma and ever/never exposure to any solvent, the OR was 0.91 (95% confidence interval: 0.74-1.11). All ORs were <1.0 for specific solvents/subgroups. There were no increases in risk according to high or low grade of tumour. CONCLUSIONS: The results of this study show no consistent associations for any solvent exposures overall or by grade of tumour.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".