Prevalence and Predictors of Patient-Reported Long-term Mental and Physical Health After Donation in the Adult-to-Adult Living-Donor Liver Transplantation Cohort Study
Bibliographic record
Abstract
BACKGROUND: Prospective and longitudinal studies have examined liver donors' medical outcomes beyond the first 1 to 2 years postdonation. There is no analogous longitudinal evidence on long-term psychosocial outcomes, including patient-reported clinically significant mental health problems and perceptions of physical well-being. We examined prevalence, descriptive characteristics, and predictors of diagnosable mental health conditions and self-reported physical health problems, including fatigue and pain, in the long-term years after liver donation. METHODS: Donors from 9 centers who initially completed telephone interviews at 3 to 10 years postdonation (mean, 5.8 years; SD, 1.9) were reinterviewed annually for 2 years using validated measures. Outcomes were examined descriptively. Repeated-measures regression analyses evaluated potential predictors and correlates of outcomes. RESULTS: Of 517 donors initially interviewed (66% of those eligible), 424 (82%) were reassessed at least once. Prevalence rates of major depression and clinically significant pain were similar to general population norms; average fatigue levels were better than norms. All prevalence rates showed little temporal change. Anxiety and alcohol use disorder rates exceeded normative rates at 1 or more assessments. Longer postdonation hospitalization, female sex, higher body mass index, concerns about donation-related health effects, and burdensome donation-related financial costs were associated with increased risk for most outcomes (P's < 0.05). Men were at higher risk for alcohol use disorder (P < 0.001). CONCLUSIONS: Anxiety and alcohol use disorders were more common than would be expected; they may warrant increased research attention and clinical surveillance. Surveillance for long-term problems in the areas assessed may be optimized by targeting donors at higher risk based on identified predictors and correlates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".