Prevalence and Predictors of Sleep Disturbance among Liver Diseases in Long-Term Transplant Survivors
Bibliographic record
Abstract
BACKGROUND: Patients with cirrhosis are known to experience sleep disturbance, which negatively impacts health-related quality of life. OBJECTIVE: To assess the prevalence and predictors of sleep disturbance before and after liver transplantation (LT). METHODS: Both pre- and post-LT patients were administered the Basic Nordic Sleep Questionnaire. The primary outcome was overall sleep satisfaction; the secondary outcomes were sleep latency and sleep duration. RESULTS: Eighty-three patients participated pre-LT and 273 post-LT. Overall, participants having completed both pre- and post-LT questionnaires reported satisfactory sleep 61% of the time before LT and 65% of the time after LT. However, on review of all questionnaires, patients with alcoholic liver disease (ETOH) experienced dramatically less sleep disturbance (OR 0.13 [95% CI 0.03 to 0.60]) post-LT, whereas those with hepatitis C remained without improvement (OR 0.90 [95% CI [0.38 to 2.15]). On logistic regression, patients with ETOH had statistically less sleep satisfaction pre-LT (OR 5.8 [95% CI 1.0 to 40.5]) and significantly better sleep satisfaction post-LT (OR 0.50 [95% CI 0.20 to 1.00]) compared with those with hepatitis C. In addition, both ETOH and other conditions had significantly better sleep latency than hepatitis C patients. CONCLUSIONS: Sleep parameters for patients who undergo LT for hepatitis C do not improve following LT as much as they do in patients transplanted for ETOH. Following LT, patients transplanted for ETOH are significantly more satisfied with their sleep than those transplanted for hepatitis C. Physicians should address and manage sleep quality after LT, so as to ultimately improve quality of life.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".