Expression of integrin-linked kinase is not a useful prognostic marker in resected hepatocellular cancer.
Bibliographic record
Abstract
BACKGROUND: Hepatocellular cancer (HCC) is one of the most common malignancies worldwide, and is known to be associated with a poor prognosis. Unfortunately there are no available reliable markers of prognosis. The aim of this study was to determine whether integrin-linked kinase (ILK) expression correlates with post-resection survival from HCC. PATIENTS AND METHODS: A tissue microarray was constructed using HCC samples, and immunohistochemical analysis for ILK was then carried out and scored by three independent observers. Clinical chart review was performed to determine survival parameters. RESULTS: Of the 52 cases of HCC, 22 cases were associated with hepatitis B (HBV), 18 with hepatitis C (HCV), 2 with HBV and HCV co-infection; 81% of all patients were male and 19% female. Western immunoblotting showed a highly significant correlation between levels of expression of ILK and ser473-PKBphosphorylation, both in control and tumor sections (Spearman rank correlation, r=0.8155, p=0.0004), however, there was no direct correlation between the levels of expression of ILK with patient survival (log-rank test, p= 0.864). CONCLUSION: ILK expression does not appear to have a role in predicting outcome in patients with resected HCC.
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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 | 0.000 | 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) | 0.001 | 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".