The serum level of NX-DCP-R, but not DCP, is not increased in alcoholic liver disease without hepatocellular carcinoma
Bibliographic record
Abstract
BACKGROUND AND AIM: Alcoholic liver disease (ALD) is the most common cause of hepatocellular carcinoma (HCC) worldwide. Des-gamma-carboxy prothrombin (DCP) is elevated in many patients with HCC, but also in severe alcoholics without HCC. We aimed to clarify whether the DCP/NX-DCP ratio (NX-DCP-R) could have a high specificity in ALD patients without HCC. METHODS: We performed a prospective cohort study on a total of 703 consecutive outpatients of liver diseases including severe alcoholics and healthy volunteers, who underwent blood biochemical examinations at Kobe University Hospital. Serum DCP was measured by electrochemiluminescence immunoassay (ECLIA) using a monoclonal antibody, MU-3. A novel parameter, serum NX-DCP, which represents predominantly DCP caused by reduced vitamin K availability, was also measured by ECLIA using monoclonal antibodies P-16 and P-11. The diagnostic accuracy of DCP and NX-DCP-R in patients with and without excessive alcohol intake was statistically examined. RESULTS: DCP was significantly higher in alcoholics than in non-alcoholics (p= 0.005), whereas the NX-DCP-R did not differ between alcoholics and non-alcoholics (p= 0.375). DCP was significantly increased in the serum of each patient with alcoholic hepatitis and alcoholic cirrhosis (p< 0.05), whereas the NX-DCP-R was not increased (p> 0.05). CONCLUSIONS: NX-DCP-R, but not DCP, was not increased in alcoholics without HCC. As for negative screening for HCC, the specificity of the NX-DCP-R in alcoholics without HCC was better than that of DCP in alcoholics without HCC, and so could be a useful negative screening tool for HCC in millions of alcoholics worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".