Serum Biomarkers Study and the Establishment of Diagnostic Models for Hepatitids B-Related HCC
Bibliographic record
Abstract
China's HCC accounts for 90% of HBV related HCC. Early detection, diagnosis and treatment are the key to effective control of HCC. By measuring the levels of expression of AFP, DCP and GP73 in the serum of HBV-related HCC patients, the diagnostic value of single and combined detection of the above indicators in HBV-related HCC shall be discussed, and the mathematical model of differential diagnosis by SVM shall be established to provide reference for the diagnosis of HBV-related HCC. A total of 301 patients and healthy persons from March 2016 to January 2018 from Beijing Tongren Hospital affiliated to Capital Medical University have been selected. These lection includes 57 cases of HBV-related HCC, 61 cases of non- HBV-related HCC, 52 cases of HBV-related cirrhosis, 57 cases of chronic HBV, and 74 healthy persons in the same period. The levels of serum DCP, AFP and GP73 in each group were measured. Combined diagnosis of three indexes is better than single diagnosis, P<0.001. Using SVM mathematical diagnosis model, the specificity and sensitivity of diagnosing HBV-related HCC and healthy controls reached 98.7% and 97.6%, while the specificity and sensitivity of diagnosing HBV-related HCC and HBV-related cirrhosis reached 90.91% and 96.3%, respectively. Serum DCP, AFP and GP73 can be used independently as a useful reference for diagnosing HBV-related HCC patients. Combined detection of the three indicators can improve the sensitivity of HBV-related HCC diagnostic test. The SVM model can be used to diagnose and identify liver diseases at different stages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".