Outcome of positive antinuclear antibodies in individuals without connective tissue disease.
Bibliographic record
Abstract
OBJECTIVE: To determine if individuals with high titer antinuclear antibodies (ANA) but without clinical evidence of connective tissue disease (CTD) subsequently develop CTD or experience a change in ANA positivity. METHODS: We included patients from an initial study database as well as those reviewed in an outpatient rheumatology clinic at the University of Alberta Hospital over the past 8 years. A telephone survey targeting signs and symptoms of CTD was conducted. Serum samples from consenting patients were then assayed for ANA, antibodies to extractable nuclear antigens (ENA), and anti-dsDNA by the Rheumatic Disease Unit at the University of Alberta Hospital. RESULTS: Sixty-two patients completed the telephone survey and 53 completed both the telephone survey and repeat serological blood investigations. Mean length of followup was 5.4 years, with an age range from 19 to 87 years. Forty-eight of 53 patients (91%) remained ANA positive on repeat testing, and 5 patients were also ENA positive. Three patients had been diagnosed with CTD since the previous study. The most common clinical features on telephone survey included joint pain (34 patients) followed by Raynaud's phenomenon (11 patients). CONCLUSION: Patients tended to remain ANA positive on repeat testing. Three out of 53 patients had developed CTD, reflecting the more sensitive but less specific nature of ANA testing. Another common condition associated with ANA positivity was hypothyroidism. Continued longterm followup with larger cohorts is needed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".