Development and Validation of a Nomogram for Predicting Postoperative Early Relapse and Survival in Hepatocellular Carcinoma
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Early relapse after hepatectomy presents a significant challenge in the treatment of hepatocellular carcinoma (HCC). The aim of this study was to construct and validate a novel nomogram model for predicting early relapse and survival after hepatectomy for HCC. PATIENTS AND METHODS: We conducted a large-scale, multicenter retrospective analysis of 1,505 patients with surgically treated HCC from 4 medical centers. All patients were randomly divided into either the training cohort (n=1,053) or the validation cohort (n=452) in a 7:3 ratio. A machine learning-based nomogram model for prediction of HCC was established by integrating multiple risk factors that influence early relapse and survival, which were identified from preoperative clinical data and postoperative pathologic characteristics of the patients. RESULTS: The median time to early relapse was 7 months, whereas the median time from early relapse to death was only 19 months. The concordance indexes of the postoperative nomogram for predicting disease-free survival and overall survival were 0.741 and 0.739, respectively, with well-calibrated curves demonstrating good consistency between predicted and observed outcomes. Moreover, the accuracy and predictive performance of the postoperative nomograms were significantly superior to those of the preoperative nomogram and the other 7 HCC staging systems. The patients in the intermediate- and high-risk groups of the model had significantly higher probabilities of early and critical recurrence (P<.001), whereas those in the low-risk group had higher probabilities of late and local recurrence (P<.001). CONCLUSIONS: This postoperative nomogram model can better predict early recurrence and survival and can serve as a useful tool to guide clinical treatment decisions for patients with HCC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it