Cancer incidence in professional flight crew and air traffic control officers: Disentangling the effect of occupational <i>versus</i> lifestyle exposures
Bibliographic record
Abstract
Flight crew are occupationally exposed to several potentially carcinogenic hazards; however, previous investigations have been hampered by lack of information on lifestyle exposures. The authors identified, through the United Kingdom Civil Aviation Authority medical records, a cohort of 16,329 flight crew and 3,165 air traffic control officers (ATCOs) and assembled data on their occupational and lifestyle exposures. Standardised incidence ratios (SIRs) were estimated to compare cancer incidence in each occupation to that of the general population; internal analyses were conducted by fitting Cox regression models. All-cancer incidence was 20-29% lower in each occupation than in the general population, mainly due to a lower incidence of smoking-related cancers [SIR (95% CI) = 0.33 (0.27-0.38) and 0.42 (0.28-0.60) for flight crew and ATCOs, respectively], consistent with their much lower prevalence of smoking. Skin melanoma rates were increased in both flight crew (SIR = 1.87; 95% CI = 1.45-2.38) and ATCOs (2.66; 1.55-4.25), with rates among the former increasing with increasing number of flight hours (p-trend = 0.02). However, internal analyses revealed no differences in skin melanoma rates between flight crew and ATCOs (hazard ratio: 0.78, 95% CI = 0.37-1.66) and identified skin that burns easily when exposed to sunlight (p = 0.001) and sunbathing to get a tan (p = 0.07) as the strongest risk predictors of skin melanoma in both occupations. The similar site-specific cancer risks between the two occupational groups argue against risks among flight crew being driven by occupation-specific exposures. The skin melanoma excess reflects sun-related behaviour rather than cosmic radiation exposure.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".