B-089 Establishing an in-house quality control program for nine autoantibody assays using donor sera
Bibliographic record
Abstract
Abstract Background Evaluation of autoantibodies in patient sera is essential to inform clinical decision-making in the classification and management of autoimmune disease. Clinical laboratories performing semi-quantitative autoantibody testing face significant challenges in developing evidence-based quality management systems to assure performance. Careful consideration is required to evaluate assay imprecision, including quality control (QC) matrix, autoantibody titer targets, and performance goals. The objective of this study was to evaluate the performance of a novel patient-based QC solution relative to vendor-provided QC for nine autoantibodies over a four-year period at a tertiary pediatric care centre. Methods Internal QC data were extracted over a 4-year period for nine autoantibodies measured via chemiluminescent immunoassays (anti-double stranded DNA (dsDNA), anti-ribonuclear protein, anti-Ro52, anti-Ro60, anti-La, anti-Smith, anti-proteinase 3, anti-myeloperoxidase, and anti-tissue transglutaminase IgA). QC evaluated during study period included vendor-based QC and third-party patient-based QC. For patient-based QC, autoantibody-positive specimens (defibrinated plasma from single human donors) were acquired by our laboratory for in-house evaluation. Following pre-testing, one individual donor specimen was selected and diluted to the desired titer using pooled immunoglobulin depleted sera and stored at -80°C until testing. Lot-specific QC coefficients of variation (CV), titer means, and standard deviations were calculated and compared across QC matrices. Results Approximately 500 QC values per autoantibody were evaluated. Mean CV across lots ranged from 8.2 to 14.4% for negative vendor-based QC, 8.2-14.5% for positive vendor-based QC, and 9.8-17.8% for positive patient-based QC across evaluated autoantibodies. Anti-dsDNA and anti-Ro52 (demonstrated a statistically significant (p-value<0.001) difference between calculated CVs for vendor-based QC (anti-dsDNA: 8.2%, anti-Ro52: 9.8%) relative to patient-based QC (anti-dsDNA: 17.8%, anti-Ro52: 15.1%). Imprecision estimates for the other autoantibodies evaluated did not demonstrate significant differences between matrices. Conclusion This study evaluates the performance of a third-party patient-based QC solution relative to vendor-based QC using real-time clinical laboratory data for nine autoantibodies measured using chemiluminescent immunoassays. These data contribute to the limited literature on practical considerations for autoantibody QC and may serve as a benchmark tool to assess autoantibody imprecision across different matrices. Based on our findings, clinical laboratories may consider supplementing vendor-based QC with single patient donor QC materials, where available, to provide an independent performance assessment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".