Identifying candidates with favorable prognosis following liver transplantation for hepatocellular carcinoma: Data mining analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The optimal cutoff of each value in configuring selection criteria for pre-transplant assessment of hepatocellular carcinoma (HCC) remains uncertain. METHODS: To build a predictive model for recurrent HCC, we performed data mining analysis on patients who underwent LT for HCC at University Health Network (n = 246). The model was externally validated using a cohort from the Scientific Registry of Transplant Recipients (SRTR) database (n = 9,769). RESULTS: Among 246 patients, 14.6% (n = 36) experienced recurrent HCC within 2.5 years post-LT. The risk prediction model for recurrent HCC identified two subgroups with low-risk (total tumor diameter [TTD] <4 cm and serum alpha-fetoprotein [AFP] <73 ng/ml, n = 135) and with high-risk (TTD >4 cm and/or AFP >73 ng/ml, n = 111). The reproducibility of the model was validated through the SRTR database; overall patient survival rate was significantly better in low-risk group than high-risk group (P < 0.0001). Using Cox regression model, this yardstick, not Milan criteria, was revealed to efficiently predict post-transplant survival independent of underlying characteristics (P < 0.0001). CONCLUSIONS: Grouping LT candidates with pre-LT HCC by the cutoffs of TTD 4 cm and AFP 73 ng/ml which were unearthed by data mining analysis efficiently classify patients according by the post-transplant prognosis.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".