Comparative Performance Analysis of 4 Different Anti-Citrullinated Protein Assays in the Diagnosis of Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the diagnostic performances of 2 recently developed assays, third-generation anti-cyclic citrullinated peptide (anti-CCP3) and anti-mutated citrullinated vimentin (anti-MCV), in comparison to conventional second-generation anti-cyclic citrullinated peptide (anti-CCP2) assay; and to assess a novel fully automated, random-access AxSYM anti-CCP assay for early diagnosis of rheumatoid arthritis (RA). METHODS: A cohort of 176 patients was enrolled in our study; 93 were diagnosed as having RA. The non-RA group consisted of 83 patients including 38 with systemic lupus erythematosus, 17 with primary Sjögren's syndrome, 11 with osteoarthritis, and 17 healthy controls. All were tested for presence of anti-CCP2, anti-CCP3, AxSYM anti-CCP, anti-MCV, and rheumatoid factor (RF)-IgM according to the manufacturers' instructions. RESULTS: Diagnostic performance of the assays revealed the highest area under the curve for the novel AxSYM anti-CCP [89.1; 95% confidence interval (CI) 84.3-93.8], followed by anti-CCP3 (86.7; 95% CI 81.6-91.9), anti-CCP2 (82; 95% CI 75.8-88.3), and anti-MCV (71.9; 95% CI 64.4-79.5). The sensitivities and specificities were 60.2% and 98.8% for anti-CCP2, 61.3% and 97.6% for anti-CCP3, 80.6% and 84.3% for AxSYM anti-CCP, 49.8% and 91.6% for anti-MCV, and 67.8% and 91.6% for RF-IgM, respectively. CONCLUSION: At cutoff of 5 U/ml, AxSYM anti-CCP emerged as a highly sensitive first-line early diagnostic tool for RA, with the greatest discrimination power, above 16 U/ml, in case of positive result. Using a single easily performed automated assay at 2 determined decision limits we were able to diagnose 81% of cases of RA and missing only 1.2%.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".