M29 Fvc decline over 1 year predicts mortality but not subsequent fvc decline in patients with ipf
Bibliographic record
Abstract
Introduction In the INPULSIS trials, nintedanib reduced disease progression by reducing FVC decline vs placebo in patients with IPF. Patients who completed an INPULSIS trial could receive open-label nintedanib in the extension trial INPULSIS-ON. Aim To assess the impact of FVC decline in INPULSIS on FVC decline and mortality in INPULSIS-ON. Methods Descriptive analysis of the proportions of nintedanib-treated patients who had FVC decline <10% or≥10% predicted (pred) from baseline to week 52 of INPULSIS and the proportions of patients in these groups who had FVC decline <10% pred,≥10% pred, or died in the first year of INPULSIS-ON. Results 430 patients received nintedanib in both INPULSIS and INPULSIS-ON. Of these, 89.1% had FVC decline <10% pred from baseline to week 52 of INPULSIS. FVC decline from baseline to week 52 in patients treated with nintedanib in INPULSIS did not predict FVC decline in the first year of INPULSIS-ON. Most patients (77.2%) had FVC decline <10% pred in the first year of INPULSIS-ON. Patients who had FVC decline ≥10% pred in INPULSIS had higher mortality in INPULSIS-ON than patients with FVC decline <10% pred. Conclusion Independent of FVC decline in the first year, most patients had FVC decline <10% pred with continued nintedanib for a second year. FVC decline ≥10% pred over 1 year did not predict subsequent FVC decline, but was associated with higher mortality. Please refer to page A261 for declarations of interest in relation to abstract M29.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".