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Record W4417507641 · doi:10.58931/crt.2025.2366

Interstitial Lung Disease for the Rheumatologist: Pearls and Insights

2025· article· W4417507641 on OpenAlexaff
Laurence Poirier-Blanchette, Océane Landon‐Cardinal, Sabrina Hoa

Bibliographic record

VenueCanadian rheumatology today. · 2025
Typearticle
Language
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsInterstitial lung diseaseRheumatoid arthritisComplicationLungDiseaseLung disease

Abstract

fetched live from OpenAlex

Interstitial lung disease (ILD) is a potentially life-threatening complication of systemic autoimmune rheumatic diseases (SARDs). Its prevalence varies according to the underlying SARD, being highest in anti-synthetase and anti‑melanoma-differentiation-associated protein 5 (MDA5) syndromes, but affecting the greatest number of individuals in rheumatoid arthritis due to its higher overall frequency. Because ILD onset may precede, coincide with, or follow SARD diagnosis, rheumatologists may uncover an undiagnosed SARD during ILD evaluation or, conversely, detect ILD through screening of patients with established SARD. The spectrum of SARD-ILD is broad: some patients have mild, stable disease, others experience slowly progressive disease, and some deteriorate rapidly despite treatment, leading to oxygen dependence, lung transplantation, or death. Drug therapies, including immunosuppressive and anti‑fibrotic agents, can slow the progression of SARD-ILD. This article addresses three key clinical questions pertinent to rheumatologists. First, we explore clinical, serological, and morphological features that can aid in diagnosing SARD in patients with ILD, offering practical pearls. Second, we examine screening—covering who to screen, when, how, and at what frequency. Finally, we outline our approach to SARD-ILD management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.248
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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