Sildenafil for idiopathic pulmonary fibrosis: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background: Patients with idiopathic pulmonary fibrosis have a poor overall prognosis and there are few evidence based drug therapies that reduce mortality. Objective: This systematic review and meta-analysis aims to assess whether sildenafil reduces mortality, reduces disease progression and the adverse side effects associated with it. Methods: In this review, randomized controlled studies (RCTs) were retrieved from MEDLINE, Cochrane, and EMBASE. The primary outcome was mortality. The secondary outcomes included change in FVC, acute exacerbations and hospitalizations and adverse drug effects leading to discontinuation. We used an inverse variance random effects meta-analysis method to calculate pooled odds ratio (OR) and standardized mean difference (SMD). Results: A total of 4 studies were included. Sildenafil probably reduces mortality when compared to placebo or to standard care, [OR 0.63 (0.38,1.03), I2=0%]. Pooled results showed sildenafil does not alter the rate of change of FVC [SMD 0.02 (-0.14,0.18)], or DLCO [SMR -0.01 (-0.18,0.17)], I2=0]. Pooled results showed sildenafil may not reduce the number of hospitalizations or acute exacerbations, [OR 1.06 (0.67,1.67)], I2= 0]. There was no significant difference in drug discontinuation due to adverse effects when comparing sildenafil to the control group, [OR 0.79 (0.56, 1.11)], I2=0]. Conclusion: Sildenafil probably reduces all-cause mortality in IPF patients. More studies need to be done in order to confirm the magnitude and reliability of the point estimate.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".