Anti-Cyclic Citrullinated Peptide Antibodies Distinguish Hepatitis B Virus (HBV)-associated Arthropathy from Concomitant Rheumatoid Arthritis in Patients with Chronic HBV Infection
Bibliographic record
Abstract
OBJECTIVE: To determine whether anti-cyclic citrullinated peptide (anti-CCP) antibodies, which are a highly specific test for rheumatoid arthritis (RA), could differentiate between hepatitis B virus (HBV)-associated arthropathy and concomitant RA in Korean patients with chronic HBV infection. METHODS: We investigated 240 patients with HBV infection. Anti-CCP antibodies were measured by ELISA and rheumatoid factor (RF) by the latex fixation test. Patient records were reviewed, and a standard form was used to record all demographic, clinical, and laboratory characteristics. Patients were divided into 4 groups according to joint symptoms: asymptomatic, arthralgia, oligoarthritis, and RA. We categorized liver disease into 3 groups: carrier, chronic hepatitis, and cirrhosis. RESULTS: Anti-CCP antibodies and RF were detected in 11 and 28 of 240 patients, respectively. Anti-CCP antibodies were detected in 9 of 10 RA (90%) and 2 of 230 non-RA patients (0.86%). The positive rate for RF was 90% in RA and 8.3% in non-RA. Eight of 10 RA patients were positive for both RF and anti-CCP antibodies. RF was detected in 11 patients without joint symptoms, 4 with arthralgia, and 4 with oligoarthritis, whereas anti-CCP antibodies were found in 1 patient without joint symptoms and 1 with oligoarthritis. Specificity of anti-CCP antibody for RA was 99.1%, whereas RF showed 91.7% specificity (p<0.0002). We compared the titers and positive detection rates of anti-CCP antibodies and RF among liver disease subgroups. There was no significant between-subgroup difference. CONCLUSION: Measurement of anti-CCP antibodies is better than RF detection to discriminate HBV-associated arthropathy from concomitant RA in patients with chronic HBV infection.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".