A Composite Endpoint of Liver Surgery (CELS): Development and Validation of a Clinically Relevant Endpoint Requiring a Smaller Sample Size
Bibliographic record
Abstract
BACKGROUND: The feasibility of trials in liver surgery using a single-component clinical endpoint is low because single endpoints require large samples due to their low incidence. The current study sought to develop and validate a novel composite endpoint of liver surgery (CELS) to facilitate the generation of more feasible and robust high-level evidence in the field of liver surgery. METHODS: Patients who underwent curative-intent hepatectomy for hepatocellular carcinoma, intrahepatic cholangiocarcinoma, or colorectal liver metastasis were identified using a multi-institutional database. Components of CELS were selected based on perioperative liver surgery-specific complications using univariable logistic regression models. The association of CELS with prolonged length of stay (LOS) and surgery-related death was evaluated and externally validated. Sample sizes were calculated for both individual outcomes and CELS. RESULTS: Among 1958 patients, 377 (19.3%) met CELS criteria based on postoperative bile leak (n = 221, 11.3%), post-hepatectomy liver failure (n = 71, 3.6%), post-hepatectomy hemorrhage (n = 38, 1.9%), or intraoperative blood loss of 2000 ml or greater (n = 101, 5.2%). CELS demonstrated favorable discriminative accuracy of surgery-related death (analytic cohort: area under the curve [AUC], 0.79 vs external validation cohort: AUC, 0.85). In addition LOS was longer among the patients with a positive CELS (analytic cohort: 14 vs. 9 days [p < 0.001] vs. the validation cohort: 10 vs. 6 days [p < 0.001]). Relative to individual endpoints, CELS allowed a 45.8-91.6% reduction in sample size. CONCLUSION: CELS effectively predicted surgery-related death and can be used as a standardized, clinically relevant endpoint in prospective trials, facilitating smaller sample sizes and enhancing feasibility compared with single quality outcome metrics.
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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 | 0.152 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".