Progressive pulmonary fibrosis: an expert group consensus statement
Bibliographic record
Abstract
This expert group consensus statement emphasises the need for standardising the definition of progressive fibrosing interstitial lung diseases (F-ILDs), with an accurate initial diagnosis being of paramount importance in ensuring appropriate initial management. Equally, case-by-case decisions on monitoring and management are essential, given the varying presentations of F-ILDs and the varying rates of progression. The value of diagnostic tests in risk stratification at presentation and, separately, the importance of a logical monitoring strategy, tailored to manage the risk of progression, are also stressed. The term "progressive pulmonary fibrosis" (PPF) exactly describes the entity that clinicians often face in practice. The importance of using antifibrotic therapy early in PPF (once initial management has failed to prevent progression) is increasingly supported by evidence. Artificial intelligence software for high-resolution computed tomography analysis, although an exciting tool for the future, awaits validation. Guidance is provided on pulmonary rehabilitation, oxygen and the use of non-invasive ventilation focused specifically on the needs of ILD patients with progressive disease. PPF should be differentiated from acute deterioration due to drug-induced lung toxicity or other forms of acute exacerbations. Referral criteria for a lung transplant are discussed and applied to patient needs in severe diseases where transplantation is not realistic, either due to access limitations or transplantation contraindications. In conclusion, expert group consensus guidance is provided on the diagnosis, treatment and monitoring of F-ILDs with specific focus on the recognition of PPF and the management of pulmonary fibrosis progressing despite initial management.
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 | 0.084 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".