Expert Consensus Guidelines on Minimally Invasive Donor Hepatectomy for Living Donor Liver Transplantation From Innovation to Implementation
Bibliographic record
Abstract
OBJECTIVE: The Expert Consensus Guidelines initiative on MIDH for LDLT was organized with the goal of safe implementation and development of these complex techniques with donor safety as the main priority. BACKGROUND: Following the development of minimally invasive liver surgery, techniques of MIDH were developed with the aim of reducing the short- and long-term consequences of the procedure on liver donors. These techniques, although increasingly performed, lack clinical guidelines. METHODS: A group of 12 international MIDH experts, 1 research coordinator, and 8 junior faculty was assembled. Comprehensive literature search was made and studies classified using the SIGN method. Based on literature review and experts opinions, tentative recommendations were made by experts subgroups and submitted to the whole experts group using on-line Delphi Rounds with the goal of obtaining >90% Consensus. Pre-conference meeting formulated final recommendations that were presented during the plenary conference held in Seoul on September 7, 2019 in front of a Validation Committee composed of LDLT experts not practicing MIDH and an international audience. RESULTS: Eighteen Clinical Questions were addressed resulting in 44 recommendations. All recommendations reached at least a 90% consensus among experts and were afterward endorsed by the validation committee. CONCLUSIONS: The Expert Consensus on MIDH has produced a set of clinical guidelines based on available evidence and clinical expertise. These guidelines are presented for a safe implementation and development of MIDH in LDLT Centers with the goal of optimizing donor safety, donor care, and recipient outcomes.
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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 | 0.118 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".