Medications for the treatment of pulmonary arterial hypertension: a systematic review and network meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it