Medications for the treatment of pulmonary arterial hypertension: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is no consensus on the most effective treatments of pulmonary arterial hypertension (PAH). Our objective was to compare effects of medications for PAH. METHODS: We searched MEDLINE, Embase, the Cochrane Central Register of Controlled Trials and Clinicaltrials.gov from inception to December 2021. We performed a frequentist random-effects network meta-analysis on all included trials. We rated the certainty of the evidence using the Grades of Recommendation, Assessment, Development, and Evaluation approach. RESULTS: We included 53 randomised controlled trials with 10 670 patients. Combination therapy with endothelin receptor antagonist (ERA) plus phosphodiesterase-5 inhibitors (PDE5i) reduced clinical worsening (120.7 fewer events per 1000, 95% CI 136.8-93.4 fewer; high certainty) and was superior to either ERA or PDE5i alone, both of which reduced clinical worsening, as did riociguat monotherapy (all high certainty). PDE5i (24.9 fewer deaths per 1000, 95% CI 35.2 fewer to 2.1 more); intravenous/subcutaneous prostanoids (18.3 fewer deaths per 1000, 95% CI 28.6 fewer deaths to 0) and riociguat (29.1 fewer deaths per 1000, 95% CI 38.6 fewer to 8.7 more) probably reduce mortality as compared to placebo (all moderate certainty). Combination therapy with ERA+PDE5i (49.9 m, 95% CI 25.9-73.8 m) and riociguat (49.5 m, 95% CI 17.3-81.7 m) probably increase 6-min walk distance as compared to placebo (moderate certainty). CONCLUSION: Current PAH treatments improve clinically important outcomes, although the degree and certainty of benefit vary between treatments.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.006 | 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".