Clinical utility of simultaneous measurement of alpha-fetoprotein and des-γ-carboxy prothrombin for diagnosis of patients with hepatocellular carcinoma in China: A multi-center case-controlled study of 1,153 subjects
Bibliographic record
Abstract
This study aimed to investigate the clinical utility of simultaneous measurement of alphafetoprotein (AFP) and des-γ-carboxy prothrombin (DCP) for hepatocellular carcinoma (HCC) diagnosis in Chinese patients predominantly caused by hepatitis B virus infection by a multi-center case-controlled study. Subjects were 1,153 individuals from three major hospitals in China, including 550 cases in HCC group, 164 in Malignant disease group, 182 in Benign disease group, 85 in Chronic liver disease group, and 173 in Normal group. Serum levels of AFP and DCP were measured and clinicopathological features were determined for all subjects. Results showed that the levels of DCP and AFP were significantly higher in HCC group (550 patients, 74.18% with HBV infection) than that in other four groups (P < 0.001). Receiver operating curves (ROC) indicated the optimal cut-off value was 86 mAU/mL for DCP with a sensitivity of 71.50% and specificity of 86.30%, and 21 ng/mL for AFP with a sensitivity of 68.00% and specificity of 93.20%. The area under ROC curve was 0.846 for DCP, 0.832 for AFP, and 0.890 for the combination of DCP and AFP. The combination of DCP and AFP resulted in a higher Youden index and a sensitivity of approximately 90%, even for small tumors. The simultaneous measurement of AFP and DCP could achieve a better sensitivity in diagnosing Chinese HCC patients, even for small tumors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".