Different indirect immunofluorescence ANA substrate performance in a diagnostic setting of patients with SLE and related disorders: retrospective review and analysis
Bibliographic record
Abstract
OBJECTIVE: Given the increasing relevance of the ANA assay to classification of SLE and the uncertainty and variation surrounding different ANA assay performance, we compared the human epithelial type 2 (HEp-2) to mouse liver (ML) substrate in our local cohort and provided a review of the evidence for their use in autoimmune rheumatic diseases (ARDs). METHODS: Electronic health record data (2003-2008) were used to identify patients who had concurrent HEp-2 and ML ANA, and a diagnosis of SLE or other ARDs. We determined the agreement between HEp-2 and ML ANA regarding positivity, titre and pattern, and their predictors. Sensitivity of HEp-2 ANA, ML ANA, repeating HEp-2 ANA, and combining HEp-2 and ML ANA assays was assessed. RESULTS: There were 961 patients with concurrent HEp-2 and ML ANA samples, including 418 SLEs. There was generally fair to moderate agreement in HEp-2 and ML ANA (kappa (κ)=0.35-0.79), titres (κ=0.34-0.79) and patterns (κ=0.35-0.93). In SLE, the presence of anti-dsDNA antibodies was predictive of ANA agreement between HEp-2 and ML ANA (adjusted OR 6.27, 95% CI 1.45 to 27.20, p=0.01). The ANA sensitivity for most ARDs was highest when the HEp-2 test was repeated, followed by when the HEp-2 and ML ANA were combined and when only the HEp-2 or ML ANAs were used. CONCLUSION: In keeping with prior studies, we demonstrated that there was fair to moderate agreement between HEp-2 and ML assays in the largest comparison of HEp-2 and ML as substrates for ANA testing in various ARDs. Furthermore, ANA sensitivity was higher when the HEp-2 assay was repeated rather than combining HEp-2 and ML.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".