Lung cancer risk among workers in the construction industry: results from two case–control studies in Montreal
Bibliographic record
Abstract
Abstract Background Given the large number of workers in the construction industry, it is important to derive accurate and valid estimates of cancer risk, and in particular lung cancer risk. In most previous studies, risks among construction workers were compared with general populations including blue and white collar workers. The main objectives of this study were to assess whether construction workers experience excess lung cancer risk, and whether exposure to selected construction industry exposures carries excess risks. We wished to address these objectives within the sub-population of blue collar workers. Methods Two case-control studies were conducted in Montreal. Combined, they included 1593 lung cancer cases and 1427 controls, of whom 1304 cases and 1081 controls had been blue collar workers. Detailed lifetime job histories were obtained and translated by experts into histories of exposure to chemical agents. The two key analyses were to estimate odds ratio (OR) estimates of lung cancer risk: a) for all blue-collar construction workers compared with other blue-collar workers, and b) for construction workers exposed to each of 20 exposure agents found in the construction industry compared with construction workers unexposed to those agents. All analyses were conducted using unconditional logistic regression adjusted for socio-demographic factors and smoking history. Results The OR for all construction workers combined was 1.11 (95 % CI: 0.90–1.38), based on 381 blue collar construction workers. Analyses of specific exposures were hampered by small numbers and imprecise estimates. While none of 20 occupational agents examined was significantly associated with lung cancer, the following agents manifested non-significantly elevated ORs: asbestos, silica, Portland cement, soil dust, calcium oxide and calcium sulfate. Conclusions Compared with other blue collar workers, there was only a slight increased risk of lung cancer for subjects who ever held an occupation in the construction industry. The analyses of agents within the construction industry produced imprecise estimates of risk, but nevertheless pointed to some plausible associations. Excess risks for asbestos and silica were in line with previous knowledge. The possible excess risks with the other inorganic dusts require further corroboration.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".