Acute exacerbations complicating interstitial lung disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to provide an update on acute exacerbations of idiopathic pulmonary fibrosis (AE-IPF), with a specific focus on new data regarding the cause, clinical features, management and prognosis of AE-IPF. In addition, the limitations of the current definition of AE-IPF are discussed and a novel classification schema is proposed. RECENT FINDINGS: AE-IPF occurs in up to 15% of IPF patients annually and has a mortality of approximately 50%. The incidence of AE-IPF is higher in patients with worse lung function and may be increased in some populations. Emerging data suggest that exacerbations may be secondary to subclinical triggers such as infection, aspiration, mechanical injury and air pollution. Management of AE-IPF typically includes high-dose corticosteroids and antimicrobials; however, there are limited data to support these or other therapies. Prevention of AE-IPF with antifibrotic medications may be feasible and warrants further study. SUMMARY: AE-IPF is associated with significant morbidity and mortality; however, there remains a paucity of clinical data. The current definition of AE-IPF has limitations and a new classification schema should be considered.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".