An Exploratory Study of Sleep Quality After Lung Transplantation Using the Pittsburgh Sleep Quality Index
Bibliographic record
Abstract
Introduction: Sleep is essential for maintaining optimal physical and mental health as it supports crucial functions such as cognition, immune system regulation, and overall well-being. A growing emphasis on the importance of sleep warrants an investigation of sleep quality after lung transplantation. Research Question: What is the overall prevalence, nature, and severity of patient-reported disrupted sleep quality after lung transplantation using the Pittsburgh Sleep Quality Index (PSQI)? Design: This study employed a single-site, exploratory, cross-sectional descriptive design involving lung transplant recipients who completed an anonymous survey. Sleep quality was assessed using the PSQI scale. Additionally, participants provided self-reported data on demographic and transplant-related variables. Results: The response rate was 38.4% (61/158) and 64% of the respondents (39/61) demonstrated PSQI >5 with a mean PSQI score of 8.07 (SD = 4.5), suggestive of poor sleep quality. Lung transplant recipients reported difficulties across all components of sleep quality with more challenges in the categories of sleep duration, sleep latency, sleep efficiency, and the use of sleep medications. Conclusion: The prevalence of poor subjective sleep quality among lung transplant recipients highlighted the importance of continued investigation into this phenomenon. Further research employing standardized measures, larger sample sizes, and longitudinal study designs is warranted to enhance understanding of poor sleep post-lung transplant. Such endeavors are crucial for informing the development of effective assessment strategies and interventions aimed at improving sleep outcomes in patients after lung transplantation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".