Worsening dyspnoea as a predictor of progression of pulmonary fibrosis
Bibliographic record
Abstract
Progressive pulmonary fibrosis (PPF), also known as progressive fibrosing interstitial lung disease (ILD), is a term used to describe progressive lung fibrosis in a patient with an ILD other than idiopathic pulmonary fibrosis (IPF) [1]. Patients with PPF often experience burdensome symptoms such as cough and dyspnoea and impairment in their quality of life [2]. Several studies have reported associations between symptoms and subsequent disease progression in patients with pulmonary fibrosis [3–5], but little is known about the relationship between changes in symptoms and outcomes including survival. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of Interest: A. Versi has nothing to disclose. Conflict of interest: The authors did not receive payment for development of this manuscript. Conflict of interest: MW reports grants to her institution from The Dutch Pulmonary Fibrosis Patients Association, The Dutch Lung Foundation, The Netherlands Organisation for Health Research and Development, The Thorax Foundation, Sarcoidosis.nl, AstraZeneca/Daiichi-Sankyo, Boehringer Ingelheim (BI), and Hoffmann-La Roche and consulting or speaker fees from AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, CSL Behring, Galapagos, Galecto, Hoffmann-La Roche, Horizon, Kinevant Sciencs, Molecure, NeRRe, Novartis, PureTech, Thyron, Trevi, Vicore. Conflict of interest: JJS reports consulting fees from Boehringer Ingelheim and is an unpaid member of the Board of Directors for Live Fully, Inc. and patientMpower. Conflict of interest: PS reports grants, personal fees and non-financial support from PPM Services and Boehringer Ingelheim; grants from Roche; personal fees from AstraZeneca, Chiesi, CSL Behring, Galapagos, Glycocore, JucaBio, Lupin, Menarini, Novartis, Pieris, Structure Therapeutics, and Veracyte; his wife is an employee of AstraZeneca. Conflict of interest: MKo reports grants from the Canadian Institute for Health Research, Roche, BI, Pieris; fees from Boehringer Ingelheim, Roche, the European Respiratory Journal, LabCorp, Bellerophon, United Therapeutics, Nitto Denko, MitoImmune, Pieris, Abbvie, DevPro Biopharma, Horizon, Algernon, CSL Behring, ShouTi. Conflict of interest: HN reports grants and consulting fees from Boehringer Ingelheim and Roche and has been a trial investigator for Galapagos, Galecto, Gilead, Novartis, Sanofi; he has participated on an endpoint committee for Galapagos. Conflict of interest: MKr reports grants, consulting fees and fees for speaking from Boehringer Ingelheim and Roche and holds leadership or fiduciary roles with Deutsche Gesellschaft für Pneumologie, the European Respiratory Society, and the German Respiratory Society. Conflict of interest: WS and KBR are employees of Boehringer Ingelheim. Conflict of interest: YI reports grants from the Japanese Ministry of Health, Labour, and Welfare and the Japan Agency for Medical Research and Development; payment for presentations from Boehringer IngelheimBI, Kyorin, Shionogi, GlaxoSmithKline, ThermoFisher; and has served as a consultant or steering committee member for Boehringer Ingelheim, Galapagos, Roche, Taiho, CSL Behring, Vicore Pharma, Savara.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".