Society for the Advancement of Transplant Anesthesia: Liver Transplant Anesthesia Fellowship—White Paper Advocating Measurable Proficiency in Transplant Specialties Training
Bibliographic record
Abstract
The anesthesia community has openly debated if the care of transplant patients was generalist or specialist care ever since the publication of an opinion paper in 1999 recommended subspecialty training in the field of liver transplantation anesthesia. In the past decade, liver transplant anesthesia has become more complex with a sicker patient population and evolving evidence-based practices. Transplant training is currently not required for accreditation or certification in anesthesiology, and not all anesthesia residency programs are associated with transplant centers. Yet there is evidence that patient outcome is affected by the experience of the anesthesiologist with liver transplants as part of a multidisciplinary care team. Requests for a formal review of the inequities in training opportunities and requirements led the Society for the Advancement for Transplant Anesthesia (SATA) to begin the task of developing post-graduate fellowship training recommendations. In this article, members of the SATA Working Group on Transplant Anesthesia Education present their reasoning for specialized education and conclusions about which pathways can better prepare trainees to care for complex transplant patients.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".