Timing of quality of life and lung function changes during the first year following lung transplantation: A multicenter prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Lung transplantation (LT) has been shown to improve lung function and quality of life (QoL). We sought to clarify if QoL improvements coincide with improvements in spirometry assessments in the first post-transplant year. METHODS: In the multicenter observational Clinical Trials in Organ Transplantation-20 study, LT recipients had longitudinal forced expiratory volume in 1 second (FEV1) and QoL measurements, specifically the St. George's Respiratory Questionnaire (SGRQ) and 36-Item Short Form Survey (SF-36), collected at 1, 3, 6, 9, and 12 months post-LT. We assessed whether best QoL scores occurred before, simultaneously, or after best FEV1. RESULTS: Of 803 recipients, 702 met the inclusion criteria. The best total SGRQ score occurred before best FEV1 in 16.0% of patients, simultaneously in 28.3%, and afterward in 55.7%. Similarly, the best SF-36 physical score occurred before best FEV1 in 18.7% of patients, simultaneously in 32.7%, and afterward in 48.6%. Single LTs, age >65 years, male sex, and diagnosis other than cystic lung disease were associated with a higher likelihood of achieving best QoL after best FEV1. CONCLUSIONS: Both spirometry and multiple physical and social QoL domains improved over the first post-LT year, but these improvements did not necessarily occur simultaneously. Nearly half of patients reached their best respiratory-specific and physical QoL scores after best FEV1; however, timing varied by recipient characteristics. As the pace of post-LT recovery is multifactorial, our findings provide insights to patients and providers regarding anticipated post-transplant changes and highlight the importance of considering both spirometry and QoL measures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".