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 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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".