Quality of life outcomes following pediatric lung transplantation
Bibliographic record
Abstract
PURPOSE: Compared to other solid organs, survival after lung transplantation (LTx) is still poor. Discussions on survival benefits following LTx in children, however, have largely concentrated on medical outcome data. Little research describes quality of life (QoL) of pediatric LTx recipients, which is partly due to the small number of pediatric LTxs performed. Only two centers worldwide performed >10 pediatric LTxs in 2013, making data on QoL in this population difficult to obtain. The primary objective was to examine the impact of LTx on QoL of pediatric recipients. METHODS: LTx recipients aged 8-17 years and their parents were recruited from a Canadian pediatric transplant centre. Participants completed the PedsQL 4.0 Generic Core Scales, a validated health-related QoL patient-reported outcome measure for children and adolescents, pre-transplant and two times post-transplant. Pre-LTx QoL scores were compared with initial assessment scores post-LTx and changes in QoL over time were described. Correlations between self- and proxy-reports were also discussed. RESULTS: Ten pediatric LTx recipients (six male, mean age = 13.3 years) and their parents were enrolled. Assessments were completed pre-LTx (mean months = 4.8) and two times post-LTx (mean months 8.7 and 24.6, respectively). Pre- and post-transplant total scores differed significantly for both self- and proxy-report, which remained consistent at a second assessment post-transplant (P = 0.018 and 0.028, respectively). CONCLUSIONS: Findings highlight the importance of QoL outcomes when exploring LTx as a treatment option. Future research should explore long-term QoL outcomes post-LTx and examine standardized integration of patient-reported outcomes into clinical practice.
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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 | 0.005 |
| 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 | 0.000 |
| 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".