Benchmarks in Liver Resection for Intrahepatic Cholangiocarcinoma
Bibliographic record
Abstract
Abstract Introduction Benchmarking in surgery has been proposed as a means to compare results across institutions to establish best practices. We sought to define benchmark values for hepatectomy for intrahepatic cholangiocarcinoma (ICC) across an international population. Methods Patients who underwent liver resection for ICC between 1990 and 2020 were identified from an international database, including 14 Eastern and Western institutions. Patients operated on at high-volume centers who had no preoperative jaundice, ASA class <3, body mass index <35 km/m2, without need for bile duct or vascular resection were chosen as the benchmark group. Results Among 1193 patients who underwent curative-intent hepatectomy for ICC, 600 (50.3%) were included in the benchmark group. Among benchmark patients, median age was 58.0 years (interquartile range [IQR] 49.0–67.0), only 28 (4.7%) patients received neoadjuvant therapy, and most patients had a minor resection (n = 499, 83.2%). Benchmark values included ≥3 lymph nodes retrieved when lymphadenectomy was performed, blood loss ≤600 mL, perioperative blood transfusion rate ≤42.9%, and operative time ≤339 min. The postoperative benchmark values included TOO achievement ≥59.3%, positive resection margin ≤27.5%, 30-day readmission ≤3.6%, Clavien-Dindo III or more complications ≤14.3%, and 90-day mortality ≤4.8%, as well as hospital stay ≤14 days. Conclusions Benchmark cutoffs targeting short-term perioperative outcomes can help to facilitate comparisons across hospitals performing liver resection for ICC, assess inter-institutional variation, and identify the highest-performing centers to improve surgical and oncologic 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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, 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".