Impact of alcohol consumption, substance use, and smoking on treatment outcomes in tuberculosis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This study aimed to elucidate the influence of alcohol, smoking, and substance use on tuberculosis (TB) treatment failure using a meta-analysis approach. METHOD: A comprehensive search strategy was developed and applied to three major databases: MEDLINE, Web of Science, and Scopus. Additionally, Google Scholar, and Google were used to locate grey literature. Studies were identified through title and abstract screening, followed by a full-text review for eligibility. The Newcastle-Ottawa Scale checklist was employed to assess the quality of included studies. Pooled odds ratios (OR) with 95% confidence intervals (CI) were calculated for each factor. RESULTS: The initial database search and other sources yielded 10,518 articles. After applying inclusion criteria, 19 studies with a total of 180,119 participants were selected for the meta-analysis. The results revealed significant associations between all three factors and treatment failure. Pooled ORs indicated that alcohol consumption (OR 2.05; 95% CI 1.65 to 2.55), smoking (OR = 1.85; 95% CI 1.44 to 2.37), and substance use (OR 2.04; 95% CI 1.63 to 2.55) were each associated with an increased risk of TB treatment failure. Additionally, the majority of included studies demonstrated high methodological quality. CONCLUSION: Our findings suggest that alcohol, smoking, and substance use are significant risk factors for unsuccessful TB treatment. To enhance TB treatment efficacy, preventive interventions aimed at reducing these behaviors before treatment initiation are recommended.
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 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 | 0.030 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.056 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".