Adult Right-Lobe Living Liver Donors: Quality of Life, Attitudes and Predictors of Donor Outcomes
Bibliographic record
Abstract
To refine selection criteria for adult living liver donors and improve donor quality of care, risk factors for poor postdonation health-related quality of life (HRQOL) must be identified. This cross-sectional study examined donors who underwent a right hepatectomy at the University of Toronto between 2000 and 2007 (n = 143), and investigated predictors of (1) physical and mental health postdonation, as well as (2) willingness to participate in the donor process again. Participants completed a standardized HRQOL measure (SF-36) and measures of the pre- and postdonation process. Donor scores on the SF-36 physical and mental health indices were equivalent to, or greater than, population norms. Greater predonation concerns, a psychiatric diagnosis and a graduate degree were associated with lower mental health postdonation whereas older donors reported better mental health. The majority of donors (80%) stated they would donate again but those who perceived that their recipient engaged in risky health behaviors were more hesitant. Prospective donors with risk factors for lower postdonation satisfaction and mental health may require more extensive predonation counseling and postdonation psychosocial follow-up. Risk factors identified in this study should be prospectively evaluated in future research.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".