The Impact of Optimal Glycemic Control on Tuberculosis Treatment Outcomes in Patients With Diabetes Mellitus: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) increases the risk of developing tuberculosis (TB), and optimal glycemic control has been shown to reduce the risk of complications and improve the TB treatment outcomes in patients with DM. OBJECTIVE: This study aims to investigate the role of glycemic control in improving TB treatment outcomes among patients with DM. METHODS: MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials databases were searched for randomized controlled trials (RCTs) assessing the impact of oral glycemic control in patients with TB who have DM. Outcomes of interest were radiological findings, treatment success, sputum positivity, and mortality. Evaluations were reported as risk ratios (RRs) with 95% CIs using weighted random-effects models. RESULTS: The analysis included 6919 patients from 7 observational studies. Our meta-analysis showed significant differences between patients with optimal glycemic control and those with poor glycemic control with regard to improved treatment outcomes (RR 1.13, 95% CI 1.02-1.25; P=.02; I²=65%), reduced sputum positivity (RR 0.23, 95% CI 0.09-0.61; P=.003; I²=66%), and fewer cavitary lesions (RR 0.59, 95% CI 0.51-0.68; P<.001; I²=0%) in radiological findings. There was no significant difference between the 2 groups in terms of mortality (RR 0.57, 95% CI 0.22-1.49; P=.25; I²=0%), multilobar involvement (RR 0.57, 95% CI 0.22-1.49; P=.25; I²=0%) on radiologic examination, and upper lobe (RR 0.94, 95% CI 0.76-1.17; P=.58; I²=0%) and lower lobe (RR 1.05, 95% CI 0.48-2.30; P=.91; I²=75%) involvement on radiologic examination. CONCLUSIONS: We concluded that optimal glycemic control is crucial for reducing susceptibility, minimizing complications, and improving treatment outcomes in patients with TB with DM. Emphasizing effective health management and health care strategies are essential in achieving this control. Integrating comprehensive care among patients with TB with DM will enhance patient outcomes and alleviate the burden of disease in this population. TRIAL REGISTRATION: PROSPERO CRD42023427362; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=427362.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".