Effect of alcohol consumption on relapse outcomes among tuberculosis patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Tuberculosis (TB) is one of the major public health issues in every country. Alcohol consumption is one of the reasons associated with the severity of symptoms and death among TB patients. The impact of alcohol use on TB relapse outcomes is still debatable. This study aimed to conduct a systematic review and meta-analysis (SR/MA) to find the link between alcohol use and TB relapse outcomes. Methods Data collection was performed from December 2021 to March 2022; and was obtained from electronic databases including CINAHL, PubMed, and Scopus. The researcher carefully searched and reviewed all the relevant research concerning drinking alcohol and relapse outcomes among TB patients. A set of inclusion and exclusion criteria was used to assess research publications. The methodological quality of eligible publications was assessed using the Newcastle–Ottawa Scale. Random meta-analysis was used to determine odds ratios (ORs) with a 95% confidence interval (CIs). The funnel plot, Begg's test, and Egger's test were employed to investigate publication bias. Results There were a total of 2,113 studies found and reviewed, and eight publications were chosen for the analysis. It was found that among TB patients with a moderate appearance of heterogeneity, drinking alcohol increases the probability of relapse (OR = 3.64; 95% CI: 2.26–5.88, p < 0.001) and mortality (OR = 1.72; 95% CI: 1.40–2.12, p < 0.001). The funnel plot, Begg's test, and Egger's test all revealed that there was no indication of publication bias. Conclusions Relapses and mortality among tuberculosis patients are considerably increased by alcohol drinking. More research into the causality of this link between the degree of alcohol use and the underlying processes is required. Systematic review registration PROSPERO [CRD 42022295865].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".