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Record W2137296328 · doi:10.1183/09031936.00137710

High prevalence of smoking among patients with suspected tuberculosis in South Africa

2010· article· en· W2137296328 on OpenAlexafffund
Laurence Brunet, Madhukar Pai, Virginia Davids, Diana Hii Ing Ling, G Paradis, Laura Lenders, Richard Meldau, Richard N. van Zyl-Smit, Greg Calligaro, Brian Allwood, Rodney Dawson, Keertan Dheda

Bibliographic record

VenueEuropean Respiratory Journal · 2010
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersFogarty International CenterMcGill University
KeywordsMedicineSmoking prevalenceTuberculosisEnvironmental healthSmoking cessationCotinineCigarette smokingPopulationCross-sectional studyNicotineInternal medicinePathology

Abstract

fetched live from OpenAlex

There is growing evidence that tobacco smoking is an important risk factor for tuberculosis (TB). There are no data validating the accuracy of self-reported smoking in TB patients and limited data about the prevalence of smoking in TB patients from high-burden settings. We performed a cross-sectional analysis of 500 patients with suspected TB in Cape Town, South Africa. All underwent comprehensive diagnostic testing. The accuracy of their self-reported smoking status was determined against serum cotinine levels. Of the 424 patients included in the study, 56 and 60% of those with active and latent TB infection (LTBI), respectively, were current smokers. Using plasma cotinine as a reference standard, the sensitivity of self-reported smoking was 89%. No statistically significant association could be found between smoking and active TB or LTBI. In Cape Town, the prevalence of smoking among patients with suspected and confirmed TB was much higher than in the general South African population. Self-reporting is an accurate measure of smoking status. These results suggest the need to actively incorporate tobacco cessation programmes into TB services in South Africa.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.596

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 integrity0.0000.001
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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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

Citations75
Published2010
Admission routes2
Has abstractyes

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