Lung Cancer Risk Among Hairdressers: A Pooled Analysis of Case-Control Studies Conducted Between 1985 and 2010
Bibliographic record
Abstract
Increased lung cancer risks among hairdressers were observed in large registry-based cohort studies from Scandinavia, but these studies could not adjust for smoking. Our objective was to evaluate the lung cancer risk among hairdressers while adjusting for smoking and other confounders in a pooled database of 16 case-control studies conducted in Europe, Canada, China, and New Zealand between 1985 and 2010 (the Pooled Analysis of Case-Control Studies on the Joint Effects of Occupational Carcinogens in the Development of Lung Cancer). Lifetime occupational and smoking information was collected through interviews with 19,369 cases of lung cancer and 23,674 matched population or hospital controls. Overall, 170 cases and 167 controls had ever worked as hairdresser or barber. The odds ratios for lung cancer in women were 1.65 (95% confidence interval (CI): 1.16, 2.35) without adjustment for smoking and 1.12 (95% CI: 0.75, 1.68) with adjustment for smoking; however, women employed before 1954 also experienced an increased lung cancer risk after adjustment for smoking (odds ratio = 2.66, 95% CI: 1.09, 6.47). The odds ratios in male hairdressers/barbers were generally not elevated, except for an increased odds ratio for adenocarcinoma in long-term barbers (odds ratio = 2.20, 95% CI: 1.02, 4.77). Our results suggest that the increased lung cancer risks among hairdressers are due to their smoking behavior; single elevated risk estimates should be interpreted with caution and need replication in other studies.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.011 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".