Efficacy and safety of hydrochlorothiazide versus chlorthalidone in patients with hypertension: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Although thiazide diuretics are widely used for treating hypertension (HTN) , the comparative efficacy and safety of hydrochlorothiazide (HCTZ) versus chlorthalidone (CTD) for the management of patients with HTN is not well-established. Materials and Methods: A thorough literature search was conducted across two electronic databases (MEDLINE and Cochrane) from inception through May 2023 to identify randomized controlled trials and/or observational double-arm studies evaluating the effects of HCTZ versus CTD on cardiovascular and safety outcomes in patients with HTN. Evaluations were reported as hazard ratios (HRs) with 95% confidence intervals (CI) and analysis was performed using a random effects model. A P-value<0.05 was considered significant in all cases. Results: From the 409 articles retrieved from initial search, four potentially relevant studies were included in the final analysis. No significant differences were noted for all the cardiovascular outcomes namely MACE, myocardial infarction (MI), stroke, hospitalization for heart failure (HHF), and angina between the HCTZ and CTD arms. However, patients using CTD had significantly higher rates of hypokalemia and hyponatremia when compared with patients using HCTZ. There was no significant difference in the risk of acute kidney injury between the two arms. Conclusion: HCTZ and CTD demonstrated similar efficacy profile with respect to cardiovascular outcomes. However, HCTZ appeared to be safer due to lower risk of electrolyte imbalances like hypokalemia and hyponatremia associated with it when compared with CTD.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".