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Record W2037117933 · doi:10.1080/14622200210141266

Relations of cotinine and carbon monoxide to self-reported smoking in a cohort of smokers and ex-smokers followed over 5 years

2002· article· en· W2037117933 on OpenAlexaff
Robert P. Murray, John E. Connett, Joseph A. Istvan, Mitchell Nides, Shelly Rempel-Rossum

Bibliographic record

VenueNicotine & Tobacco Research · 2002
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversity of Manitoba
FundersNational Heart, Lung, and Blood Institute
KeywordsCotinineMedicineCohortSmoking cessationNicotineCohort studyRandomized controlled trialEnvironmental healthInternal medicinePathology

Abstract

fetched live from OpenAlex

We describe the persistence of discrepancies between biochemical measures of smoking and self-reported smoking status in a cohort of clinical trial participants across 5 years. The Lung Health Study, a randomized trial in 10 clinical centers in North America, enrolled 3923 participants in smoking intervention and 1964 in usual care in 1987 and 1988. Smoking status was assessed at baseline and at five annual follow-up visits by self-report, salivary cotinine and expired-air carbon monoxide. Compared to self-report, sensitivity and specificity of cotinine and carbon monoxide were similar across 5 years. Evidence of error in self-reports of quitting smoking persisted across 5 years, although it declined over time. Multivariate models confirmed that self-report bias was characteristic of the early years in the study. Significant covariates differed between cotinine and carbon monoxide models. When cotinine was used for verification, about half of the individuals in the smoking intervention group with self-report bias at the first year continued to exhibit bias for 5 years. In absolute terms, the errors associated with measurement were small, but they persisted over 5 years. Some differences appeared to be related to the distinction that carbon monoxide verification was immediate, while cotinine verification was deferred.

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)0.001
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 score0.004
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.064
GPT teacher head0.359
Teacher spread0.295 · 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

Citations60
Published2002
Admission routes1
Has abstractyes

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