Lack of Specificity of Anticyclic Citrullinated Peptide Antibodies in Advanced Human Immunodeficiency Virus Infection
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence and specificity of anticyclic citrullinated peptide antibodies (anti-CCP) and rheumatoid factor (RF) for rheumatoid arthritis (RA) in human immunodeficiency virus (HIV) infection and to evaluate the effect of immune reconstitution on these markers. METHODS: Patients with advanced HIV infection without arthritis were enrolled. CD4+ T lymphocyte counts (CD4), anti-CCP, and RF were determined before initiating antiretroviral therapy (ART) and repeated after 6 months. Results were compared to those of healthy controls. Patients were followed for the development of RA for 1 year. RESULTS: Sixty patients and 26 controls were studied. Six-month followup results were available on 49 patients. Mean (SD) levels of anti-CCP were higher in patients with HIV compared to controls: respectively, 9.50 (11.41) versus 0.80 (1.32) units (p < 0.001). Mean (SD) levels decreased to 4.85 (8.12) units (p = 0.006) after 6 months of ART (HIV-infected group). Fifteen percent of patients initially tested positive for anti-CCP, 4% after 6 months versus no controls (p = 0.031). Forty-seven percent of patients initially tested positive for RF, 18% after 6 months versus 8% of controls (p < 0.001). Decreases in RF and anti-CCP after ART were accompanied by increased mean (SD) CD4: from 129 (56) to 278 (140) cells/mm(3) (p < 0.001). Anti-CCP and RF positivity was not associated with the development of RA. CONCLUSION: Increased titers of anti-CCP and RF occur in advanced HIV infection. Although more specific than RF, before immune reconstitution, anti-CCP is an unreliable diagnostic marker for RA and does not necessarily predict future RA. After immune reconstitution, the specificity of anti-CCP approaches that of a control group.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".