Bibliographic record
Abstract
BACKGROUND: Interstitial lung disease (ILD) comprises a heterogeneous group of inflammatory and fibrotic conditions, often resulting in progressive lung function decline and increased mortality. Connective tissue disease (CTD) should be considered in all patients with ILD, as distinguishing between CTD-ILD and other forms of fibrotic lung disease has important therapeutic and prognostic implications. The idiopathic inflammatory myopathies (IIM) represent a CTD subtype of growing interest to ILD experts. The expansion and availability of myositis-specific and myositis-associated antibody testing has allowed for improved disease detection and characterization. CONTENT: In this review, we highlight the relationship between myositis antibodies and ILD. Select forms of IIM, such as the antisynthetase syndrome and clinically amyopathic dermatomyositis can present with rapidly progressive ILD, warranting timely disease diagnosis and management. Disease phenotypes, prevalence, laboratory testing, prognosis, and management strategies are described according to select myositis antibodies. SUMMARY: Myositis antibodies provide valuable information for clinicians managing patients with ILD. This review aims to increase awareness of their role in disease detection, pathophysiology, and possibly therapeutics.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".