The Effect of Diuretics on Patients with Heart Failure: A Network Meta-Analysis: Diuretics Effect on Heart Failure Patients
Bibliographic record
Abstract
PURPOSE: We aimed to comprehensively evaluate the curative effect of torasemide, tolvaptan, furosemide and azosemide on patients with heart failure. METHODS: Relevant studies were retrieved by searching the electronic databases until May 2018. Quality assessment and data extraction of selected studies were evaluated by two reviewers. Heterogeneity across studies was assessed utilizing the I2 statistic and Q- test, and appropriate effect model was selected to calculate the pooled effect size. Network meta-analysis was conducted and the convergence degree of model was evaluated. RESULTS: A total of 12 studies were enrolled in this study. Significant heterogeneity was not identified across the studies. Significantly greater differences were found in left ventricular ejection fraction (LVEF) for furosemide VS. azosemide, in brain natriuretic peptide (BNP) for furosemide VS. azosemide and furosemide VS. torasemide, and in adverse effects for furosemide VS. torasemide through Meta-analysis of direct comparison. In addition, network meta-analysis results suggested there were no significant differences in adverse effects, mortality, BNP and LVEF among these groups. However, the relatively low mortality and small improvement of BNP and LVEF were found in HF patients treated with torasemide. CONCLUSION: Torasemide might be an optimal treatment for HF patients considering its comprehensive curative effect.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.037 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".