Rheumatological Assessment Is Important for Interstitial Lung Disease Diagnosis
Bibliographic record
Abstract
OBJECTIVE: Interstitial lung diseases (ILD) form a diverse group of parenchymal lung disorders. Currently, a multidisciplinary team (MDT) including pulmonologists, radiologists, and pathologists is the gold standard for ILD diagnosis. Recently, additional subtypes of connective tissue disease (CTD)-ILD with autoimmune features were defined, making the rheumatological assessment increasingly important. We aimed to assess the effect of adding a rheumatologist to the MDT for routine rheumatology assessment. METHODS: A prospective study that assessed newly diagnosed ILD patients by 2 parallel blinded arms; all patients were evaluated by both MDT (e.g., history, physical examination, blood tests, pulmonary function tests, and biopsies, if needed) and a rheumatologist (e.g., history, physical examination, blood and serological tests). RESULTS: Sixty patients were assessed with the mean age of 67.3 ± 12 years, 55% male, and 28% smokers. The rheumatological assessment reclassified 21% of the idiopathic pulmonary fibrosis as CTD. Moreover, the number of CTD-ILD with autoimmune features was increased by 77%. These included antineutrophil cytoplasmic antibody-associated vasculitis, antisynthetase syndrome, and IgG4-related ILD. Retrospectively, rheumatological evaluation could have saved 7 bronchoscopies and 1 surgical biopsy. CONCLUSION: Adding routine rheumatology assessments could significantly increase diagnostic accuracy and reduce invasive procedures.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".