Significance of phenotype change after chronic lung allograft dysfunction onset
Bibliographic record
Abstract
Definitions for chronic lung allograft dysfunction (CLAD) phenotypes were recently revised (2019 ISHLT consensus). Post-CLAD onset phenotype transition may occur as a result of change in obstruction, restriction, or RAS-like opacities (RLO). We aimed to assess the prevalence and prognostic implications of these transitions. This was a single-center, retrospective cohort study of bilateral lung transplants performed in 2009-2015. CLAD phenotypes were determined per ISHLT guidelines. CLAD phenotype transition was defined as a sustained change in obstruction, restriction or RLO. We specifically focused on phenotype changes based on RLO emergence. Association of RLO development with time to death or retransplant were assessed using Kaplan-Meier and Cox proportional hazards models. Among 211 patients with CLAD, 47 (22.2%) experienced a phenotype transition. Nineteen patients developed RLO. Development of RLO phenotype after CLAD onset was associated with a shorter time to death/retransplant when considering the entire CLAD patient cohort (HR = 4.00, CI 2.74-5.83, P < 0.001) and also when restricting the analysis to only patients with a Non-RLO phenotype at CLAD onset (HR 9.64, CI 5.52-16.84, P < 0.0001). CLAD phenotype change based on emergence of RAS-like opacities implies a worse outcome. This highlights the clinical importance of imaging follow-up to monitor for phenotype transitions after CLAD onset.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".