Do worse scores on patient-reported outcomes predict the progression of interstitial lung disease (ILD)?*
Bibliographic record
Abstract
Background The INBUILD trial enrolled patients with progressive fibrosing ILDs other than IPF. It is unclear whether, as observed in patients with IPF, patients with fibrosing ILDs who have worse scores on patient-reported outcomes assessing symptoms or health-related quality of life (HRQL) are at greater risk of ILD progression. Aim To assess associations between patient-reported outcomes at baseline and progression of fibrosing ILDs in the INBUILD trial. Methods Associations between the King's Brief ILD (K-BILD) questionnaire total score and Living with Pulmonary Fibrosis (L-PF) questionnaire symptoms dyspnoea domain score at baseline and time to ILD progression (absolute decline in FVC ≥10% predicted) or death during the INBUILD trial were assessed using Cox’s regression models. Results At baseline, mean (SD) K-BILD total and L-PF symptoms dyspnoea domain scores were 52.4 (10.5) (n=662) and 21.7 (18.0) (n=652), respectively, on scales of 0–100. Median exposure to trial drug was 17.4 months. In both treatment groups, baseline K-BILD total scores or L-PF symptoms dyspnoea domain scores that indicated worse HRQL were associated with a higher risk of ILD progression or death during the trial (Figure). Conclusions In patients with progressive fibrosing ILDs, worse scores on patient-reported outcomes are associated with a higher risk of ILD progression or death. Abb. 1 Figure. Associations between K-BILD questionnaire systoms dyspnoea domain score at baseline and risk of ILD progression or death in the INBUILD trial*presented at ERS 2021; presenting on behalf of the authors Publication History Article published online: 11 May 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".