Lung transplantation outcomes of patients with interstitial pneumonia with autoimmune features: a single-centre retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Interstitial pneumonia with autoimmune features (IPAF) describes patients with interstitial lung disease (ILD) and autoimmune features without meeting criteria for a specific rheumatic disease. No longitudinal data exist on post-transplant outcomes in IPAF patients. We compared baseline demographics, pre-transplant characteristics and post-transplant outcomes between IPAF and idiopathic pulmonary fibrosis (IPF) patients undergoing double lung transplantation. METHODS: We retrospectively analysed lung transplant recipients with ILD in British Columbia between 1 January 2014 and 30 April 2024. Diagnoses of IPAF and IPF were made by multidisciplinary review. Continuous variables were analysed using the Mann-Whitney U test, categorical variables with Fisher's exact test, and survival using Kaplan-Meier analysis. RESULTS: We identified 20 IPAF and 64 IPF patients. IPAF patients were more likely female (50% vs 17%, P = 0.006), on pre-transplant immunosuppression (60% vs 6.3%, P < 0.001) and were less likely to receive antifibrotics (20% vs 64%, P < 0.001). No difference was seen in 1-year or cumulative survival, though survival curves diverged over time favouring IPAF. Post-transplant lung function, acute rejection, infection-related hospitalization, malignancy and chronic lung allograft dysfunction (CLAD) were similar, with non-usual interstitial pneumonia (UIP) IPAF exhibiting a survival advantage over IPF (100% vs 66%, P = 0.044). Explant pathology revealed more UIP patterns in IPF, while IPAF showed more non-specific interstitial pneumonia (NSIP) or unclassifiable patterns. CONCLUSIONS: Post-transplant survival, lung function and complication rates were comparable between IPAF and IPF patients at one year and the last follow-up. This is the first study to report both short- and long-term lung transplant outcomes in IPAF patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".