What's in a name? That which we call IPF, by any other name would act the same
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) remains a truly idiopathic fibrotic disease, with a modest genetic predilection and candidate triggers but no overall explanation for the development of disease in non-familial cases. Agreement on terminology has contributed to major clinical and translational advances since the millennium. It is likely that the entity currently captured by the term "IPF" will be radically reclassified over the next decade, either through "splitting" (into IPF subgroups responding selectively to individual disease-modifying agents) or through "lumping" of IPF with other forms of progressive fibrotic lung disease (with shared pathogenetic mechanisms and IPF-like disease behaviour). In this perspective, we summarise the clinical and pathogenetic justification for a focus on "the progressive fibrotic phenotype" in future clinical and translational research. By this means, we can hope to address the needs of non-IPF patients with inexorably progressive fibrotic disease, currently disenfranchised by lack of access to agents that are efficacious in IPF. In this regard, ongoing trials of anti-fibrotic therapies in non-IPF patients with progressive fibrosis may be highly influential. Future revision of IPF nomenclature may be warranted if there are major conceptual changes but without compelling justification, the benefits of renaming IPF are likely to be outweighed by the resulting confusion.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".