Defining Benchmarks for Major Liver Surgery
Bibliographic record
Abstract
OBJECTIVE: To measure and define the best achievable outcome after major hepatectomy. BACKGROUND: No reference values are available on outcomes after major hepatectomies. Analysis in living liver donors, with safety as the highest priority, offers the opportunity to define outcome benchmarks as the best possible results. METHODS: Outcome analyses of 5202 hemi-hepatectomies from living donors (LDs) from 12 high-volume centers worldwide were performed for a 10-year period. Endpoints, calculated at discharge, 3 and 6 months postoperatively, included postoperative morbidity measured by the Clavien-Dindo classification, the Comprehensive Complication Index (CCI), and liver failure according to different definitions. Benchmark values were defined as the 75th percentile of median morbidity values to represent the best achievable results at 3 month postoperatively. RESULTS: Patients were young (34 ± [9] years), predominantly male (65%) and healthy. Surgery lasted 7 ± [2] hours; 2% needed blood transfusions. Mean hospital stay was 11.7± [5] days. 12% of patients developed at least 1 complication, of which 3.8% were major events (≥grade III, including 1 death), mostly related to biliary/bleeding events, and were twice higher after right hepatectomy. The incidence of postoperative liver failure was low. Within 3-month follow-up, benchmark values for overall complication were ≤31 %, for minor/major complications ≤23% and ≤9%, respectively, and a CCI ≤33 in LDs with complications. Centers having performed ≥100 hepatectomies had significantly lower rates for overall (10.2% vs 35.9%, P < 0.001) and major (3% vs 12.1%, P < 0.001) complications and overall CCI (2.1 vs 8.5, P < 0.001). CONCLUSIONS: The thorough outcome analysis of healthy LDs may serve as a reference for evaluating surgical performance in patients undergoing major liver resection across centers and different patient populations. Further benchmark studies are needed to develop risk-adjusted comparisons of surgical outcomes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".