Prevalence and prognosis of unclassifiable interstitial lung disease
Bibliographic record
Abstract
To the Editor: We read with interest the recent article by Ryerson et al. [1], describing the prevalence and characteristics of patients with unclassifiable interstitial lung disease (ILD) presenting to a specialist centre. This study is the first to target specifically this newly defined disease category, in parallel with publication of the updated American Thoracic Society/European Respiratory Society classification of the idiopathic interstitial pneumonias (IIPs) [2]. The authors identified 10% of their ILD patient population as having unclassifiable ILD following multidisciplinary discussion (MDD). The major reasons for diagnostic uncertainty related to either inability or unwillingness of the patient to undergo surgical lung biopsy, or inadequacy of the tissue specimen sampled. Only a minority of cases remained ambiguous after a reasonable tissue sample had been obtained. The study detailed the clinical characteristics of this hybrid group, with many of the mean baseline demographics and disease behaviours falling between the two reference groups of patients with confirmed idiopathic pulmonary fibrosis (IPF) and non-IPF diagnoses. Multivariate analysis revealed low diffusing capacity of …
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".