Perioperative Changes in Serum Transaminase Levels
Bibliographic record
Abstract
OBJECTIVES: To define how dynamic changes in pre versus postoperative serum aspartate aminotransferase (AST) and alanine transaminase (ALT) levels may impact postoperative morbidity after curative-intent resection of hepatocellular carcinoma (HCC). BACKGROUND: Hepatic ischemia/reperfusion can occur at the time of liver resection and may be associated with adverse outcomes after liver resection. METHODS: Patients who underwent curative resection for HCC between 2010 and 2020 were identified from an international multi-institutional database. Changes in AST and ALT (CAA) on postoperative day 3 versus preoperative values ( ) were calculated using the formula: based on a fusion index through the Euclidean norm, which was examined relative to the Comprehensive Complication Index (CCI). The impact of CAA on CCI was assessed by the restricted cubic spline regression and Random Forest analyses. RESULTS: A total of 759 patients were included in the analytic cohort. Median CAA was 1.7 (range: 0.9-3.25); 431 (56.8%) patients had a CAA <2 215 (28.3%) patients with CAA 2 to 5, and 113 (14.9%) patients had CAA ≥5. The incidence of postoperative complications was 65.0% (n = 493) with a median CCI of 20.9 (interquartile range: 20.9-33.5). Spline regression analysis demonstrated a nonlinear incremental association between CAA and CCI. The optimal cutoff value of CAA was 5, identified by the recursive partitioning technique. After adjusting for other competing risk factors, CAA ≥5 remained strongly associated with the risk of postoperative complications (reference CAA <5, odds ratio: 1.63, 95% CI: 1.05-2.55, P = 0.03). In fact, the use of CAA to predict postoperative complications was very good in both the derivative (area under the curve: 0.88) and external (area under curve: 0.86) cohorts (n = 1137). CONCLUSIONS: CAA was an independent predictor of CCI after liver resection for HCC. The use of routine laboratories, such as AST and ALT, can help identify patients at the highest risk of postoperative complications after HCC resection.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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, 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".