Psychosocial assessment tools for use before transplantation are predictive of post-operative psychosocial and health behavior outcomes: a narrative review of the literature
Bibliographic record
Abstract
Introduction: In end-stage diseases, transplantation may be necessary. The limited number of donors led to the development of several pre-transplant psychosocial assessment tools. We summarized the predictive value of these tools before solid-organ transplantation. Methods: The PRISMA search strategy and the MEDLINE database were used to review the literature. From 1,050 records, we found thirteen studies using four different scales (Millon Behavioral Health Inventory [MBHI], Psychosocial Assessment of Transplant Candidates [PACT], Stanford Integrated Psychosocial Assessment for Transplantation [SIPAT], and Transplant Evaluation Rating Scale [TERS]). Results: TERS and MBHI were associated with the highest number of positive studies concerning pre-transplant scores and primary outcomes. Psychosocial scales predict in a systematic way psychosocial and health behavioural outcomes, but generated mixed results for mortality and rejection. Discussion: This narrative review underlines the need for multidisciplinary evaluation and well-conducted clinical trials to assist transplant teams in utilizing psychosocial evaluation effectively during evaluation of candidates.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".