A65 SYSTEMATIC REVIEW OF PROGNOSTIC SCORES FOR RECURRENCE OF HEPATOCELLULAR CARCINOMA AFTER LIVER TRANSPLANTATION
Bibliographic record
Abstract
Recurrence of hepatocellular carcinoma (HCC) after liver transplantation is a major cause of morbidity and mortality. Several risk assessment tools have been developed, although to date, there is no widely accepted tool to predict HCC recurrence. The aim of the current study was to critically appraise published literature on clinicopathologic prognostic scoring systems assessing recurrence of HCC after transplantation. An electronic data base search was performed using MEDLINE, EMBASE, Cochrane library, and Central Registry of Clinical Trials. All retrospective chart reviews and validation studies that analyzed prognostic scoring systems consisting of clinical and explant pathologic characteristics to determine recurrence risk of hepatocellular carcinoma post-transplant between 2000–2017 were included. The literature search identified 145 studies, of which 134 were excluded. From the 11 scoring systems, 6 had been validated. 7 out of the 11 assigned weight to the presence of microvascular invasion. All studies included tumor diameter in their respective scoring systems. Several scoring systems exist to classify risk of HCC recurrence post-transplant. Head to head comparison study would be beneficial in identifying which prognostic score has the best predictive ability. None
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".