AASLD Program (pp. 27A-77A)
Bibliographic record
Abstract
Review the criteria and evaluation process for donors and recipients.Understand the basic technical aspects of the surgery.Describe the post-operative outcomes and complications in donors and recipients.Gain insight into the impact of MELD on live donor liver transplantation.Discuss the role of LDLT in hepatitis C, hepatocellular carcinoma and re-transplantation.Adult-to-adult right hepatic lobe LDLT has rapidly emerged as a treatment option for selected patients with end-stage liver disease.Approximately lop) of all liver transplantations performed in the United States utilize a living donor.This course is designed to inform physicians about the current topics in this rapidly changing field.From the medical standpoint, the process of donor and recipient selection will be discussed, including radiologic imaging techniques, financial aspects and the psychosocial assessment of donors.The technical aspects of the surgery will be described along with the postoperative complications in donors and recipients.Three current controversial issues in LDLT will be explored: hepatocellular carcinoma, hepatitis C, regulation of the procedure and use of live donors for retransplantation.The etlucal considerations and objections to LDLT will be reviewed, as well as the future role of LDLT in liver transplantation.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.160 | 0.110 |
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".