Association between tobacco smoking and drug-resistant tuberculosis
Bibliographic record
Abstract
BACKGROUND: Tobacco smoking is a risk factor for tuberculosis but little is known about the relationship between tobacco smoking and drug-resistant tuberculosis (DR-TB). We undertook a systematic review and meta-analysis to quantitatively assess the association between DR-TB and tobacco smoking. METHODS: We searched for relevant studies in the Ovid MEDLINE, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure, WANFANG, and WEIPU data-bases from inception to September 1, 2017. Results were expressed as odds ratios (ORs) with accompanying 95% CIs, and subgroup analyses were performed by study design, smoking type, DR-TB type, and multivariate analysis. RESULTS: Thirty-three studies related to tobacco smoking and DR-TB were included. We found substantial evidence that tobacco smoking is associated with an increased risk of DR-TB (OR 1.57, 95% CI 1.33-1.86). Associations were also found in subgroup analyses: for multidrug-resistant tuberculosis (OR 1.49, 95% CI 1.19-1.86) and for any DR-TB (OR 1.70, 95% CI 1.3-2.23); the pooled OR was 1.45 (95% CI 1.11-1.90) for current smoking, 2.25 (95% CI 1.46-3.47) for past smoking, and 1.56 (95% CI 1.22-1.98) for smoking history; and similar ORs were also observed in study design and multivariate analysis subgroup analysis. CONCLUSION: This study demonstrated that tobacco smoking is an independent risk factor for DR-TB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".