Home kitchen ventilation, cooking fuels, and lung cancer risk in a prospective cohort of never smoking women in <scp>S</scp>hanghai, <scp>C</scp>hina
Bibliographic record
Abstract
Indoor air pollution (IAP) caused by cooking has been associated with lung cancer risk in retrospective case-control studies in developing and rural countries. We report the association of cooking conditions, fuel use, oil use, and risk of lung cancer in a developed urban population in a prospective cohort of women in Shanghai. A total of 71,320 never smoking women were followed from 1996 through 2009 and 429 incident lung cancer cases were identified. Questionnaires collected information on household living and cooking practices for the three most recent residences and utilization of cooking fuel and oil, and ventilation conditions. Cox proportional hazards regression estimated the association for kitchen ventilation conditions, cooking fuels, and use of cooking oils for the risk of lung cancer by hazard ratios (HR) with 95% confidence intervals (95% CI). Ever poor kitchen ventilation was associated with a 49% increase in lung cancer risk (HR: 1.49; 95% CI: 1.15-1.95) compared to never poor ventilation. Ever use of coal was not significantly associated. However, ever coal use with poor ventilation (HR: 1.69; 95% CI: 1.22-2.35) and 20 or more years of using coal with poor ventilation (HR: 2.03; 95% CI: 1.35-3.05) was significantly associated compared to no exposure to coal or poor ventilation. Cooking oil use was not significantly associated. These results demonstrate that IAP from poor ventilation of coal combustion increases the risk of lung cancer and is an important public health issue in cities across China where people may have lived in homes with inadequate kitchen ventilation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".