The association of glycemic level and prevalence of tuberculosis: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Diabetes is a well-known risk factor for tuberculosis and poorly glycemic control may increase the risk of tuberculosis. We performed a meta-analysis to explore the association of glycemic control in diabetic patients and their tuberculosis prevalence. METHODS: We included observational studies that investigated the prevalence of tuberculosis associated with glycemic control. The markers of glycated hemoglobin A1c (HbA1c) and fasting plasma glucose were used to evaluate the exposure of interest in the study. We searched related articles in PubMed, EMBASE and Web of Science through 14 December 2019. The Newcastle-Ottawa scale was used to assess the risk of bias of included studies. RESULTS: Seventeen studies (four cohort studies, five case-control studies and eight cross-sectional studies) were included, involving 1,027,074 participants. The meta-analysis found the pooled odds ratio of prevalent tuberculosis increased a 2.05-fold (95%CI: 1.65, 2.55) for the patients with HbA1c ≥7.0% compared to those with HbA1c concentration < 7.0%. Furthermore, we found the mean of HbA1c was higher in the diabetes mellitus with tuberculosis group than the diabetes-only group (P = 0.002). In the sensitivity analysis, the finding remains consistent. CONCLUSION: Our study provides the evidence that poorly controlled diabetes in diabetics may be associated with increased prevalence of tuberculosis. More efforts should focus on screening tuberculosis in uncontrolled diabetes.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.056 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".