Guidelines to manage liver transplant recipients: time for consensus?
Bibliographic record
Abstract
A good academic survey should ask questions in such a way as to evaluate practices and identify gaps or inconsistencies in practice as well as to explore opportunities for research, ultimately to improve care.Surveys can generate hypotheses, promote debate, and form the basis for quality improvement initiatives and ultimately lead to improved outcomes.In this edition of the Journal, Carrier et al. 1 have shown striking variation in practice with regards to care of patients undergoing liver transplantation.Using robust statistical methods, they show that we remain in a state of equipoise with regards to optimal management of liver transplant recipients in several domains of care.Our interventions are based, at best, on ''physiologic paradigms and inferences from low-quality studies.''Furthermore, it is probable that institutional norms and expectations (which are powerful phenomena) direct practices in the absence of evidence-based guidelines.The late American poet and musician Jim Morrison commented that he liked people who ''shake other people up and make them feel uncomfortable.''I suspect Jim Morrison would approve of Carrier et al. as this survey should make all of us who bear the privilege and responsibility of caring for patients during liver transplantation feel uncomfortable and it should shake us up.Uncertainty is not a bad thing according to Franc ¸ois-Marie Arouet, who, under the nom de plume Voltaire, is famously credited with the observation that, while uncertainty is an uncomfortable position, certainty is an absurd position.Approximately 33,000 patients worldwide are estimated to undergo liver transplantation each year.Data from 2020 revealed that approximately 12,000 liver transplants were performed in the USA and 600 in Canada. 2 In 2021, 589 liver transplants were performed in Canada, while 536 patients remained on the waiting list for a liver transplant.Ninety-five of these 536 patients (16%) died on the waiting list. 3he operative cost per liver transplant in Canada, not including pre-or posttransplant care of the recipient, ranges from CAD 31,000 to CAD 48,000. 4ortality for liver transplantation remains significant, depending on a variety of premorbid, perioperative, and graft-related factors.Overall mortality lies between 8% and 20%. 5,6Morbidity is harder to measure discretely, but acute hepatic necrosis, prolonged intensive care unit length of stay, postoperative liver and kidney dysfunction, and infections, which are all factors associated with mortality, are clearly associated with morbidity and, unsurprisingly, increased costs.From this we can safely conclude that liver transplantation is an expensive, resource-consuming, and high-stakes undertaking.As co-stewards of these resourceconsuming undertakings, we are obligated to ensure we do our best to reduce mortality and morbidity and provide the best possible milieu to facilitate the best possible outcomes for patients undergoing liver transplantation.Based on the survey results from Carrier et al., we should absolutely feel uncomfortable knowing that our practices are varied, without consensus, and not based on quality evidence but rather on physiologic paradigms, inferences from low quality evidence, and institutional norms.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.075 | 0.075 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".