Staging of hepatocellular carcinoma: assessment of the CLIP, Okuda, and Child-Pugh staging systems in a cohort of 257 patients in Toronto
Bibliographic record
Abstract
BACKGROUND: A new staging system for hepatocellular carcinoma (HCC) has recently been reported from Italy (CLIP classification). It combines Child-Pugh staging with tumour criteria: tumour morphology, portal invasion, and alpha fetoprotein levels. AIMS: To validate the use of the CLIP staging in a cohort of HCC patients and compare it with Okuda staging. PATIENTS AND METHODS: A retrospective analysis of patients with HCC diagnosed in the Toronto General Hospital between October 1994 and December 1998. RESULTS: A total of 313 patients were identified; 19 patient with insufficient data and 37 transplant patients were excluded. Hence 257 patients in whom complete data for clinical staging were available were included in the study. The median survival of the cohort was 22.8 months. The CLIP stage 0 group (23.1% of the cohort) and the Okuda stage 1 group (50.7% of the cohort) had a five year survival rate of 67% and 35%, respectively (p<0.02). The CLIP stage 0 criteria more accurately defined patients with a good prognosis. The Okuda classification failed to identify two thirds of the 37 patients with a poor prognosis, who were identified by the CLIP criteria. Patients with a CLIP score > or =4 shared a very poor prognosis (median survival 1-3 months). Further classification above stage 4 was unnecessary. SUMMARY: The CLIP classification for HCC is easy to implement and more accurate than the Okuda classification. Our cohort was different from the CLIP cohort (more hepatitis B) but the results were still consistent.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".