Quantifying Risk to Flight Attendants from Secondhand Smoke Exposure in Airline Cabins Using Pharmacokinetic Modeling: A Case Report
Bibliographic record
Abstract
Background: Several studies of the health problems incurred by flight attendants flying during the smoking years concluded that they suffered elevated rates of chronic bronchitis, heart disease, skin cancer, breast cancer, melanoma, reproductive cancers, middle ear infections, hearing loss, asthma, pneumonia, chronic obstructive pulmonary disease, various pulmonary function abnormalities, plus depression and anxiety. Aims: Systematic review of secondhand smoke risks to flight attendants, exemplified using a specific case involving a deceased flight attendant who suffered from a multiplicity of tobacco-smoke-related diseases, including asthma, breast cancer, carotid artery stenosis, cataracts, cervical cancer, chronic obstructive pulmonary disease, coronary artery disease, laryngeal cancer, pneumonia and chronic myeloid leukemia. The decedent died in 2014 at age 68, losing an estimated 18.5 years of life expectancy. Methods: Pharmacokinetic modeling was used for the first time to estimate the risk from secondhand smoke for flight attendants on typical passenger aircraft flown by the decedent during an 18 year period ending in 1988. Results: Based on in-flight cotinine dosimetry measured in an Air Canada study, typical flight attendants would have inhaled a dose-equivalent of fine particle air pollution exceeding the “Air Pollution Emergency” levels of the U.S. Environmental Protection Agency’s Air Quality Index. The secondhand smoke cotinine dose for typical flight attendants in aircraft cabins is estimated to have been 6-fold that of the average US worker and 14-fold that of the average person. Thus, ventilation systems massively failed to control secondhand smoke air pollution in aircraft cabins, and led to extreme exposures. The decedent’s estimated lifetime cancer risk from secondhand smoke was 18 times U.S. OSHA’s Significant Risk of Material Impairment of Health level of 1 per 1000 per working lifetime. Conclusions: In-flight exposure to toxic and carcinogenic tobacco smoke in smoky passenger cabins was the major risk factor leading to the decedent’s multiple smoking-related diseases, and her premature death. This has implications for the extant and future health of the cohort of surviving flight attendants exposed to secondhand smoke on aircraft during the 20th Century Era.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".