Adult liver transplant anesthesiology practice patterns and resource utilization in the United States: Survey results from the society for the advancement of transplant anesthesia
Bibliographic record
Abstract
INTRODUCTION: Liver transplant anesthesiology is an evolving and expanding subspecialty, and programs have, in the past, exhibited significant variations of practice at transplant centers across the United States. In order to explore current practice patterns, the Quality & Standards Committee from the Society for the Advancement of Transplant Anesthesia (SATA) undertook a survey of liver transplant anesthesiology program directors. METHODS: Program directors were invited to participate in an online questionnaire. A total of 110 program directors were identified from the 2018 Scientific Registry of Transplant Recipients (SRTR) database. Replies were received from 65 programs (response rate of 59%). RESULTS: Our results indicate an increase in transplant anesthesia fellowship training and advanced training in transesophageal echocardiography (TEE). We also find that the use of intraoperative TEE and viscoelastic testing is more common. However, there has been a reduction in the use of veno-venous bypass, routine placement of pulmonary artery catheters and the intraoperative use of anti-fibrinolytics when compared to prior surveys. CONCLUSION: The results show considerable heterogeneity in practice patterns across the country that continues to evolve. However, there appears to be a movement towards the adoption of specific structural and clinical practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".