Pulmonary Fibrosis with Autoimmune Features and Sjögren’s Syndrome
Bibliographic record
Abstract
Objectives This retrospective study aims to compare the characteristics and outcomes of patients with interstitial lung disease (ILD) who have Sjögren’s syndrome-associated antibodies (SjS-aAb), with or without a diagnosis of Sjögren’s syndrome (SjS). Methods Patients evaluated at the CHUM ILD clinic with at least 1 SjS-aAb were included in the study. They were classified as having SjS if they met the 2016 ACR-EULAR criteria. Patients without SjS were further stratified as interstitial pneumonia with autoimmune features (IPAF) and non-IPAF, based on the 2015 ERS/ATS criteria.[1] ILD progression was defined according to OMERACT[2] and ATS/ERS/JRS/ALAT[3] guidelines. Results A total of 41 patients with SjS, 21 with IPAF, and 26 with non-IPAF were identified. Mean age was 71 years and 85% were White. Non-IPAF patients were more frequently men (62% vs 35%, p=0.03) with a smoking history (88% vs 60%, p=0.21) and usual interstitial pneumonia (UIP) radiologic pattern (81% vs 24%, p=0.08). SjS patients more frequently had xerophtalmia (64% vs 17%), xerostomia (55% vs 38%), anti-SSA/Ro (84% vs 35%), anti-SSB/La (32% vs 18%) and very strongly positive anti-Ro52 aAb (76% vs 40%). ILD severity was similar across groups. Immunosuppressants were used in 68%, 44% and 29% of SjS, IPAF and non-IPAF. Over a mean follow-up of 3.5 years, ILD progression was observed in 41-49% of patients and similar across groups. However, non-IPAF patients more frequently became oxygen-dependent (58% vs 34%). Lung transplants were required in 2%, and 14% died of pulmonary causes. Progressors more frequently used immunosuppressive drugs (69% vs 43%). Conclusion In this population with SjS-aAb, ILD progression was similar irrespective of SjS diagnosis and IPAF classification. Further study is required to identify predictors of progression while accounting for the effect of treatments and confounding by indication. *Drs Hoa and Landon-Cardinal contributed equally to this abstract. [1.] Fisher A. ERJ 2015;46:976-87. [2.] Khanna D. J Rheumatol 2015;42:2168-71. [3.] Raghu G. Am J Respir Crit Med 2022;205:e18-47.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".