Effects of Recipient Age and Diagnosis on Health-related Quality-of-Life Benefit of Lung Transplantation
Bibliographic record
Abstract
RATIONALE: The average age of lung transplant recipients is increasing, and the mix of recipient indications for transplantation is changing. OBJECTIVES: To determine whether the health-related quality-of-life (HRQL) benefit of lung transplantation differs by recipient age and diagnosis. METHODS: In this prospective cohort study, we obtained serial HRQL measurements in adults with advanced lung disease who subsequently underwent lung transplantation (2004-2012). HRQL assessments included the St. George's Respiratory Questionnaire, 36-Item Short-Form Health Survey (SF-36), EQ-5D, Standard Gamble, and Visual Analog Scale for current health. We used linear mixed effects models for associations between age or diagnosis and changes in HRQL with transplantation. To address potential survivorship bias, we fitted Markov models to the distribution of discrete post-transplant health states (HRQL better than pretransplant, not better, or dead) and estimated quality-adjusted life-years post-transplant. MEASUREMENTS AND MAIN RESULTS: A total of 430 subjects were listed, 387 were transplanted, and 326 provided both pretransplant and post-transplant data. Transplantation conferred large improvements in all HRQL measures: St. George's change of -47 units (95% confidence interval, -48 to -44), 36-Item Short-Form Health Survey physical component summary score of 17.7 (16.5-18.9), EQ-5D of 0.27 (0.24-0.30), Standard Gamble of 0.48 (0.44-0.51), and Visual Analog of 44 (42-47). Age was not associated with meaningful differences in the HRQL benefits of transplantation. There was less HRQL benefit in interstitial lung disease than in cystic fibrosis. CONCLUSIONS: Lung transplantation confers large HRQL benefits, which vary by recipient diagnosis, but do not differ substantially in older recipients.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".