Bibliographic record
Abstract
COMMENTARY ON: Des-gamma-carboxy Prothrombin and Alpha fetoprotein as Biomarkers for the Early Detection of Hepatocellular Carcinoma. Lok AS, Sterling RK, Everhart JE, Wright EC, Hoefs JC, Di Bisceglie AM, Morgan TR, Kim HY, Lee WM, Bonkovsky HL, Dienstag JL; HALT-C Trial Group . Reprinted from Gastroenterology . 2009 Oct 20 [Epub ahead of print], with permission from Elsevier . Abstract: Background & Aims . The outcome of patients with hepatocellular carcinoma (HCC) remains poor because of late diagnosis. The aim of this study was to compare the accuracy of alpha-fetoprotein (AFP) and des-gamma-carboxy prothrombin (DCP) in the early diagnosis of HCC. Methods: Among 1031 patients randomized in the Hepatitis C Antiviral Long-term Treatment Against Cirrhosis (HALT-C) Trial, a nested case-control study of 39 HCC cases (24 early stage) and 77 matched controls was conducted to compare the performance of AFP and DCP. Testing was performed on sera from 12months prior (month-12) to the time of HCC diagnosis (month 0). Results: The sensitivity and specificity of DCP at month 0 was 74% and 86%, respectively, at a cut-off of 40mAU/ml and 43% and 100%, respectively, at a cutoff of 150mAU/ml. The sensitivity and specificity of AFP at month 0 was 61% and 81% at a cutoff of 20ng/ml and 22% and 100% at a cutoff of 200ng/ml. At month-12, the sensitivity and specificity at the low cutoff was 43% and 94%, respectively, for DCP and 47% and 75%, respectively, for AFP. Combining both markers increased the sensitivity to 91% at month 0 and 73% at month 12, but the specificity decreased to 74% and 71%, respectively. Diagnosis of early HCC was triggered by surveillance ultrasound in 14, doubling of AFP in 5, and combination of tests in 5 patients. Conclusions: Biomarkers are needed to complement ultrasound in the detection of early HCC, but neither DCP nor AFP is optimal.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".