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Record W2588727010

Risk of lung cancer from traditional heating and cooking fuels in Montreal, Canada.

2006· article· en· W2588727010 on OpenAlexaboutno aff
Agnihotram V. Ramanakumar, Marie‐Élise Parent, Jack Siemiatycki

Bibliographic record

VenueCancer Epidemiology and Prevention Biomarkers · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsStoveEnvironmental healthIndoor air qualityLung cancerMedicineSolid fuelPopulationWaste managementSmokeEnvironmental scienceCombustionEnvironmental engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

B63 Among the major sources of indoor air pollution are combustion by-products from heating and cooking. There has been increasing concern that the use of polluting heating and cooking sources can contribute to cancer risk. In Canada, most cooking and heating is now, done with electricity or natural gas, but in the past, and still in some areas, coal and wood stoves were used for heating and gas and wood for cooking. In the course of a case-control study of lung cancer carried out in Montreal in 1996-2000, we collected information on subjects9 lifetime exposure to such sources of domestic pollution. The study included both males and females, 1205 cases and 1541 population controls had available for analysis. For male, the uni-variate analysis shows that there were some indications, albeit not statistically significant, that exposure to traditional heating and cooking sources carried some excess risk of lung cancer; but when adjusted with other covariates there was no indication of excess risks. For women, risks adjusted for smoking and other covariates were almost doubled for those exposed to traditional heating and cooking fuels. Given the paucity of evidence from developed countries, our results suggest that there might be an increased risk of lung cancer among women exposed to traditional heating and cooking fuels.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.020
GPT teacher head0.267
Teacher spread0.246 · 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.

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
Published2006
Admission routes1
Has abstractyes

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