Acute exacerbation of idiopathic pulmonary fibrosis: international survey and call for harmonisation
Bibliographic record
Abstract
Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is an often deadly complication of IPF. No focussed international guidelines for the management of AE-IPF exist. The aim of this international survey was to assess the global variability in prevention, diagnostic and treatment strategies for AE-IPF.Pulmonologists with ILD expertise were invited to participate in a survey designed by an international expert panel.509 pulmonologists from 66 countries responded. Significant geographical variability in approaches to manage AE-IPF was found. Common preventive measures included antifibrotic drugs and vaccination. Diagnostic differences were most pronounced regarding use of Krebs von den Lungen-6 and viral testing, while high-resolution computed tomography, brain natriuretic peptide and D-dimer are generally applied. High-dose steroids are widely administered (94%); the use of other immunosuppressant and treatment strategies is highly variable. Very few (4%) responders never use immunosuppression. Antifibrotic treatments are initiated during AE-IPF by 67%. Invasive ventilation or extracorporeal membrane oxygenation are mainly used as a bridge to transplantation. Most physicians educate patients comprehensively on the severity of AE-IPF (82%) and consider palliative care (64%).Approaches to the prevention, diagnosis and treatment of AE-IPF vary worldwide. Global trials and guidelines to improve the prognosis of AE-IPF are needed.
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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 | 0.026 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".