Application of Linear Discriminant Analysis in Performance Evaluation of Extractable Nuclear Antigen Immunoassay Systems in the Screening and Diagnosis of Systemic Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
This study applied a linear discriminant analysis model to evaluate the performance of 2 types of commercially available extractable nuclear antigen (ENA) immunoassays for the screening and diagnosis of systemic autoimmune rheumatic diseases (SARDs) in a large tertiary hospital reference laboratory: (1) an enzyme-linked immunosorbent assay (ELISA) and (2) a multiplex bead-based immunoassay (MPBI). The results of the study showed both ENA immunoassays had comparable sensitivity for the detection of SARDs compared with the antinuclear antigen immunofluorescence (ANA-IF) method (ANA-IF: 85.6%, ENA-ELISA: 91.5%, ENA-MPBI: 83.1%, pairwise comparisons with ANA-IF: P > .05). However, both ENA immunoassays offered improved specificity compared with the ANA-IF (ANA-IF: 24.2%; ENA-ELISA: 39.8%; ENA-MPBI: 53.1%; pairwise comparison with ANA-IF: P < .001). The use of a more specific screening immunoassay with comparable sensitivity to ANA-IF is important in a tertiary hospital with high prevalence of non-SARD immune diseases. Diagnostic performance of the ENA/dsDNA components by the MPBI and ELISA methods did not differ significantly (area under the curve [AUC], 81.0% vs 83.0%, respectively, P > .05), but the key ENA/dsDNA variables contributing to the discriminating power of the assays for the diagnosis of specific SARDs were reagent/method dependent.
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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 | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".