CONTRASTING EVIDENCE WHEN USING HOSPITAL OR POPULATION CONTROLS: THE EXAMPLE OF THE ASSOCIATION BETWEEN EXPOSURE TO GASOLINE AND DIESEL EXHAUST, AND LUNG CANCER
Bibliographic record
Abstract
ISEE-407 Introduction: Much of the evidence for an association between exposure to gasoline and diesel exhausts and lung cancer comes from occupational case-control studies. The choice of controls is critical in providing valid estimates. There is no such thing, however, as a perfect control group. Although hospital controls may be less subject to reporting bias and show higher response rates than population controls, diseases in the control pool may themselves be associated with the exposures of interest, leading to biased estimates. Do the two types of control groups tend to give similar answers? Methods: In the 1980s, we conducted a large population-based case-control study to assess the role of occupational exposures and circumstances on cancer incidence in Montreal. Incident cases from all area hospitals were ascertained. We focus here on the results from an in-depth analysis of our data on gasoline and diesel exhausts and lung cancer. For this analysis, we used 857 lung cancer cases and two distinct control series: one consisted of 533 controls from the general population, and the other, comprising 1349 patients with cancers at sites other than the lung. All subjects were interviewed to obtain a detailed job history and relevant data on potential confounders. A team of chemists and hygienists translated each job into a list of potential exposures. Analyses were carried out for exposure to gasoline and diesel exhausts, as well as for occupations presumed to have entailed exposure to those agents. Results: The odds ratios (OR) for lung cancer associated with nonsubstantial and substantial exposure to gasoline exhaust were 0.9 and 0.9, respectively, using either population or cancer controls. However, for diesel engine emissions, the two control groups yielded somewhat different estimates. Using population controls, the OR was 1.1 [95% confidence intervals (95% CI): 0.7-1.7] for nonsubstantial exposure, and 1.6 (95% CI: 0.9-2.8) for substantial exposure. Using cancer controls, the corresponding values were 1.0 (95% CI: 0.7-1.4) and 1.0 (95% CI: 0.7-1.5). Discussion: Few studies offer the opportunity to contrast results obtained with several control groups. Although they may be difficult to reconcile, our results underline the challenge in obtaining both valid occupational exposure information and representative control subjects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".