Clinical and Serological Analysis of Patients with Positive Anticyclic Citrullinated Peptide Antibodies Referred Through a Rheumatology Central Triage System
Bibliographic record
Abstract
OBJECTIVE: Anticitrullinated protein antibodies (ACPA) are a highly specific and sensitive biomarker for the diagnosis of rheumatoid arthritis (RA). Some patients who were found to have a positive ACPA test were referred to our Rheumatology Central Triage (CT; Calgary, Alberta, Canada) for assessment by a rheumatologist. The objectives of our study were to determine the clinical accuracy of ACPA in establishing a diagnosis of RA in a real-time clinical setting. METHODS: Cases that met 3 criteria were included in the study: (1) referred to the CT over 3 calendar years (n = 20,389), (2) reason for referral was a positive ACPA test (n = 568), and (3) evaluated by a certified rheumatologist (n = 314). An administrative serological database was used to retrieve specific ACPA results. RESULTS: Of patients referred through our CT for evaluation of a positive ACPA test, 57.6% received a diagnosis of RA; the remainder had a variety of other diagnoses, some of which might be considered early RA (9%). The predictive values of ACPA for the diagnosis of RA were increased when rheumatoid factor (RF) results were included in the analysis. When definite and possible RA were combined and the prevalence of moderate/high ACPA was compared to all other individuals, the positive and negative predictive values for moderate/high ACPA for RA were 74.3% and 68.4%, respectively. CONCLUSION: About 58% of patients with a positive ACPA referred through a triage system for a rheumatologist opinion received a diagnosis of RA at their first visit. RF provides additional useful information to guide the diagnosis and urgency of referral.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".