Exposures in painting related occupations and risk of selected cancers: Results from a case–control study in montreal
Bibliographic record
Abstract
BACKGROUND: Painters are considered to be at increased risk of lung cancer. The objective was to evaluate risk of several cancers, apart from lung, in painting-related professions. Most previous studies have focused on the job title rather than on exposures incurred. METHODS: A large population based case-control study was carried out during 1979-1986 in Montreal including several types of cancer and focusing on occupational exposures. Interviews elicited detailed lifetime job histories; those were evaluated by a team of industrial hygienists to assign exposure. The exposure checklist included three paint-related substances: metal coatings, wood varnishes and stains, and wood and gypsum paints. Seven types of cancer were analyzed (numbers interviewed): esophagus (97), stomach (248), colorectal (754), prostate (438), bladder (478), kidney (174) and non-Hodgkin's lymphoma (215). For each cancer type, a pooled control group was constituted from 533 population controls and 533 cancer patients selected from other types of cancer. Odds ratios (ORs) were estimated between each of the paint-related agents and each of the seven cancer types, adjusting for several potential confounders, including smoking. RESULTS: The job title of "painters" was not associated with risk of any of the cancers under study. Most of the ORs between the three agents and the seven cancers were close to null. However, there was a tendency for ORs to be above 1.0 for subjects who had substantial exposure to metal coatings, with noteworthy associations for cancers of the esophagus (OR = 4.2; 95% CI: 1.1-17.0; n = 4), prostate (OR = 2.7; 95% CI: 1.0-7.7; n = 13), and bladder (OR = 1.7; 95% CI: 0.7-4.4; n = 13). CONCLUSION: These results are compatible with an absence of risk among painting-related professions; they are also compatible with excess risk of certain cancers, especially among those exposed to metal coatings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".