Risk evaluation and recipient selection in adult liver transplantation: A mixed-methods survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it