Nintedanib, Pirfenidone and Pirfenidone Versus Nintedanib: A Systematic Review And Meta-Analysis
Bibliographic record
Abstract
Abstract Background: Patients with idiopathic pulmonary fibrosis have a poor overall prognosis. Only nintedanib and pirfenidone have been shown to reduce mortality. Objective: This systematic review and meta-analysis aims to assess the efficacy of nintedanib, pirfenidone, and pirfenidone vs nintedanib on patient important outcomes. Methods: Randomized trials 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 relative risk (RR), standardized mean difference (SMD) and mean difference (MD).Results: A total of 13 studies were included. Both nintedanib [RR 0.63 (0.47,0.85); moderate certainty] and pirfenidone [RR 0.68 (0.47,0.99); moderate certainty] probably reduce all-cause mortality when compared to placebo, but only nintedanib [SMD 0.47 (0.34, 0.60); high certainty] reduces change in FVC. Nintedanib [RR 0.69 (0.48,0.99); moderate certainty],but not pirfenidone probably reduces acute exacerbations or hospitalizations compared to placebo. Compared with placebo, neither nintedanib nor pirfenidone increased risk of drug discontinuation due to adverse effect but there is probably risk of patient drug discontinuation with pirfenidone compared to nintedanib [RR 4.34 (1.72 to 10.98); moderate certainty].Conclusion: Both nintedanib and pirfenidone probably reduce all-cause mortality. Nintedanib is probably more tolerable to pirfenidone in regard to compliance and may be more effective than pirfenidone in reducing mortality rate and in slowing disease progression. Larger head to head randomized trials are needed.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.005 | 0.005 |
| 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".