Repeat Testing of Antibodies and Complements in Systemic Lupus Erythematosus: When Is It Enough?
Bibliographic record
Abstract
OBJECTIVE: Patients with systemic lupus erythematosus (SLE) frequently undergo repeat testing for antibodies against extractable nuclear antigens (anti-ENA), but it is not known whether this is necessary or cost-effective. This study characterized the frequencies of changes in anti-ENA, anti-dsDNA, and complement C3 and C4 upon repeat testing. METHODS: Chart review was done at one site of 130 patients with SLE enrolled in the 1000 Canadian Faces of Lupus prospective registry with annual antibody and complement testing. We determined the frequency of seroconversion (changes) on the next test and over the entire followup given 1 or multiple consistent results, and the cost to detect these changes. RESULTS: Overall, 89.4% of patients had no changes in anti-ENA screening results from the first available test, 3.3% changed from negative to positive, and 7.3% from positive to negative. Following a single anti-ENA test, 3.9% of negative tests changed to positive and 4.2% of positive changed to negative on the next test. After multiple consistent tests, the frequencies of changes progressively declined. No changes from the first test were observed in anti-dsDNA, C3, and C4 in 60.8%, 83.3%, and 75.4% of patients, respectively. After 2 consistent anti-ENA tests, the cost to detect 1 change was above US$2000. CONCLUSION: Anti-ENA results change infrequently, especially following 1 or more negative tests. The high cost and lack of evidence that changes affect management suggest that repeating anti-ENA tests routinely is unnecessary. Anti-dsDNA and complements change more frequently after an abnormal result, but less after a normal value.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".