Clinical, Methodological, and Practical Considerations for Algorithmic Testing in Autoimmune Serology
Bibliographic record
Abstract
BACKGROUND: Autoimmune serology tests are central to the classification, screening, diagnosis, and monitoring of a variety of autoimmune disorders. To improve the appropriateness of serologic evaluation and support laboratory resource utilization, reflex testing approaches have been proposed and implemented across clinical laboratories. Reflex testing involves a staged approach where an initial test result triggers subsequent tests based on prespecified rules. CONTENT: Various reflex testing approaches in the context of antinuclear antibody-associated rheumatic disease, antineutrophil cytoplasmic autoantibody-associated vasculitis, celiac disease, and myasthenia gravis are reviewed here. Clinical, analytical, and practical considerations of reflex testing implementation are addressed as well as associated limitations and challenges. SUMMARY: Serology reflex testing algorithms for the evaluation of autoimmune diseases can support clinical diagnosis and laboratory resource use but may be challenging to implement and are often applied variably across institutions. Assessments of evidence-driven guidelines, clinical impact, and impact on laboratory workflow are essential to this task.
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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 | 0.061 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".