Clinical and Serological Features of Patients Referred through a Rheumatology Triage System because of Positive Antinuclear Antibodies
Bibliographic record
Abstract
BACKGROUND: The referral of patients with positive anti-nuclear antibody (ANA) tests has been criticized as an inappropriate use of medical resources. The utility of a positive ANA test in a central triage (CT) system was studied by determining the autoantibody profiles and clinical diagnoses of patients referred to rheumatologists through a CT system because of a positive ANA test. METHODS: Patients that met three criteria were included: (1) referred to Rheumatology CT over a three year interval; (2) reason for referral was a "positive ANA"; (3) were evaluated by a certified rheumatologist. The CT clinical database was used to obtain demographic and clinical information and a serological database was used to retrieve specific ANA and/or extractable nuclear antigen (ENA) test results. Clinical information was extracted from the consulting rheumatologist's report. RESULTS: 15,357 patients were referred through the CT system; 643 (4.1%) of these because of a positive ANA and of these 263 (40.9%) were evaluated by a certified rheumatologist. In 63/263 (24%) of ANA positive patients, the specialist provided a diagnosis of an ANA associated rheumatic disease (AARD) while 69 (26.2%) had no evidence of any disease; 102 (38.8%) had other rheumatologic diagnoses and 29 (11%) had conditions that did not meet AARD classification criteria. Of ANA positive archived sera, 15.1% were anti-DFS70 positive and 91.2% of these did not have an AARD. CONCLUSIONS: This is the first study to evaluate the serological and clinical features of patients referred through a CT system because of a positive ANA. The spectrum of autoantibody specificities was wide with anti-Ro52/TRIM21 being the most common autoantibody detected. Approximately 15% of referrals had only antibodies to DFS70, the vast majority of which did not have clinical evidence for an AARD. These findings provide insight into the utility of autoantibody testing in a CT system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".