Effect of antitubercular medications on blood pressure control in chronic kidney disease patients with tuberculosis: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Previous anecdotal reports suggested a decrease in antihypertensive medication potency after starting antitubercular medications. This interaction could be unpredictable in presence of renal failure due to increased half-lives of most commonly used antihypertensive medications. METHODS: In a cohort study involving 135 patients with chronic kidney disease (CKD), 62 patients with tuberculosis star-ted on antitubercular medications (TB group) were prospectively compared with 73 CKD controls (with no TB and not on antitubercular medications) for a change in antihypertensive medications. Antihypertensive dose was converted to unit score. RESULTS: The TB group had a greater increase in antihypertensive medication dose as compared with controls (89% vs. 54%, p<0.0001). In absolute terms an overall increase in antihypertensive medications was observed in 60% of pa-tients in the TB group, with a 2-fold dose increase from the baseline (p<0.0001). Four patients from the TB group de-veloped a hypertensive emergency. In multivariate linear regression, the association between TB group and increase in antihypertensives remained significant ( beta =0.38; p<0.0001). CONCLUSIONS: In CKD patients, antihypertensive medication potency is reduced in TB patients on antitubercular the-rapy in a significant number of patients, to a clinically significant degree with a potential risk for hypertensive emergency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".