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CONTRASTING EVIDENCE WHEN USING HOSPITAL OR POPULATION CONTROLS: THE EXAMPLE OF THE ASSOCIATION BETWEEN EXPOSURE TO GASOLINE AND DIESEL EXHAUST, AND LUNG CANCER

2004· article· en· W1987276101 on OpenAlexaffabout
Marie‐Élise Parent, Marie Rousseau, Jack Siemiatycki, Paolo Boffetta, Aaron Cohen

Bibliographic record

VenueEpidemiology · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCarcinogens and Genotoxicity Assessment
Canadian institutionsUniversité de MontréalArmand Frappier Museum
Fundersnot available
KeywordsLung cancerEnvironmental healthConfoundingMedicinePopulationDiesel fuelOdds ratioDiesel exhaustDemographyCancerIncidence (geometry)OncologyInternal medicineEngineeringWaste managementMathematics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.351
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2004
Admission routes2
Has abstractyes

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