Risk of active tuberculosis in chronic kidney disease: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Although the global prevalence of chronic kidney disease (CKD) is increasing, the relationship between CKD and active TB is not well described. OBJECTIVE: To conduct a systematic review to evaluate active TB risk in CKD populations. METHODS: We searched Ovid Medline, EMBASE and Cochrane databases and relevant journals to identify multicentre or regional studies reporting quantitative effect estimates of an association between CKD and active TB. Risk ratios and rate ratios were used as common measures of association. Pooled estimates were generated using a random-effects model. RESULTS: Of 3406 papers screened, 12 eligible studies were identified with 71,374 end-stage renal disease (ESRD) patients and 560 TB cases. Meta-analysis of adjusted rate ratio data in dialysis populations showed an increased rate of 3.62 (95%CI 1.79-7.33, P < 0.001) compared to the general population, while unadjusted risk ratio data in transplant populations showed an increased risk of 11.35 (95%CI 2.97-43.41) compared to the general population. CONCLUSION: We found consistent evidence of an increased risk of active TB in ESRD compared to the general population. This relationship persisted despite variability in study population, design and renal replacement therapy (RRT) modality. Further research into the role of comorbidities, RRT modality and CKD stage is required to better understand the association between CKD and active 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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".