Risk evaluation and recipient selection in adult liver transplantation: A mixed-methods survey
Bibliographic record
Abstract
Background: Liver transplant (LT) is the definitive treatment for end-stage liver disease. Limited resources and important post-operative implications for recipients compel judicious risk stratification and patient selection. However, little is known about the factors influencing physicians' assessment regarding patient selection for LT and risk evaluation. Methods: We conducted a mixed-methods, cross-sectional survey involving Canadian hepatologists, anesthesiologists, LT surgeons, and French anesthesiologists. The survey contained quantitative questions and a vignette-based qualitative substudy about risk assessment and patient selection for LT. Descriptive statistics and qualitative content analyses were used. Results: We obtained answers from 129 physicians, and 63 participated in the qualitative substudy. We observed considerable variability in risk assessment prior to LT and identified many factors perceived to increase the risk of complications. Clinicians reported that the acceptable incidence of at least 1 severe post-operative complication for a LT program was 20% (95% CI: 20-30%). They identified the presence of any comorbidity as increasing the risk of different post-operative complications, especially acute kidney injury and cardiovascular complications. Frailty and functional disorders, severity of the liver disease, renal failure and cardiovascular comorbidities prior to LT emerged as important risk factors for post-operative morbidity. Most respondents were willing to pursue LT in patients with grade III acute-on-chronic liver failure but were less often willing to do so when faced with the uncertainty of a clinical example. Conclusions: Clinicians had a heterogeneous appraisal of the post-operative risk of complications following LT, as well as factors considered in risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".