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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".