The INPULSIS trials of idiopathic pulmonary fibrosis treatment: explaining further discrepancies on exacerbations
Bibliographic record
Abstract
In a recent paper on the INPULSIS trials, two duplicate 1-year randomised controlled trials evaluating nintedanib in the treatment of idiopathic pulmonary fibrosis (IPF) [1], we provided the methodological explanation for the apparent inconsistent results arising from the two different definitions of exacerbations used in those studies [2]. These trials reported vastly different findings in their pooled analysis [1]. Indeed, the risk of an investigator-reported acute exacerbation was lower by 36% with nintedanib compared with placebo but not statistically significant (p=0.08), while, after adjudication according to a complex definition involving multiple criteria, the risk was lower by 68% and statistically significant (p=0.001). We explained that such differences in risk reductions and in statistical significance are simply the result of including outcome events that are not actual exacerbations, leading to the phenomenon of treatment effect dilution and false nonsignificant effects [2]. The correct risk reduction of 68% based on the accurate adjudicated events is diluted to a less impressive and now nonsignificant risk reduction of 36% when “false” events were added in the same proportion to both groups. Adjudication of acute exacerbations of IPF in clinical trials is crucial for accurate treatment effectiveness
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.
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 | 0.037 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.035 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".