Bibliographic record
Abstract
PURPOSE OF REVIEW: Accurate diagnosis of interstitial lung diseases (ILDs) can be challenging, and a substantial percentage of ILD patients remain unclassifiable even after thorough assessment by an experienced multidisciplinary team. In this review, we summarize the recent literature on the definition, prevalence, diagnosis, treatment, and prognosis of unclassifiable ILD, and also discuss important current issues and provide future perspectives on the classification of ILD. RECENT FINDINGS: Approximately 12% of patients with ILD are considered unclassifiable, with large variability across previous studies that is in part secondary to inconsistent definitions of unclassifiable ILD and other ILD subtypes. A recent International Working Group suggested that unclassifiable ILD should be defined by the absence of a leading diagnosis that is considered more likely than not after multidisciplinary discussion of all available information. Clinical features and outcomes of unclassifiable ILD are intermediate between idiopathic pulmonary fibrosis and nonidiopathic pulmonary fibrosis ILD cohorts, and choices for pharmacotherapy should be considered on a case-by-case basis. SUMMARY: Recent studies have provided additional data on the clinical features and prognosis of unclassifiable ILD, but also highlight the many uncertainties that still exist in ILD diagnosis and classification. New tools are needed to more accurately characterize patients with unclassifiable ILD.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".