Analysis of lung cancer incidence relating to air pollution levels adjusting for cigarette smoking: a case-control study
Bibliographic record
Abstract
A case-control study (lung cancer: 2711; age and sex matched non-lung cancer controls: 2711) encompassing the years 1986-2004 was undertaken further to the results of increased mortality and cancer incidence in Windsor, Canada.The objective was to investigate associations between lung cancer incidence and exposure to air pollutants controlling for cigarette smoking and duration of residence.A nominal file of cases and controls ascertained by the Ontario Cancer Registry was obtained from Cancer Care Ontario; smoking information and addresses of residence within Windsor were obtained respectively from medical charts at the Windsor Regional Cancer Centre and from the Windsor City Directories.For each case and control and for each air pollutant, a cumulative exposure was calculated based on duration of residence at each address location; the centroid of postal codes of addresses was used as proxy for residential location.For each location, annual NO 2 and SO 2 levels were estimated, using Land Use Regression based on results from 54 monitors within Windsor.Results obtained from conditional likelihood regression for matched or stratified data, (including sex, age at diagnosis, numbers of cigarette smoked per day and duration of smoking as covariates) are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".