Real-world Experience of Using<i>HLA-B*27</i>Tag-single-nucleotide Polymorphism Assay to Screen for Axial Spondyloarthritis
Bibliographic record
Abstract
We previously published an analytical validation of the HLA-B*27 tag-single-nucleotide polymorphism (SNP) assay in The Journal 1 based on the initial identification of the HLA-B*27 tag-SNP by the International Genetics of Ankylosing Spondylitis Consortium2. Our tag-SNP assay is much cheaper than the traditional HLA-B locus testing, and has been implemented at the Provincial Medical Genetics Laboratory of Eastern Health (St. John’s, Newfoundland, Canada) since August 1, 2016. The data on the use of this assay have been systematically collected. Currently, HLA-B locus testing is the gold standard for determining HLA-B*27 status, but it is rather expensive as a first-line test3. With an analytical sensitivity of 97.6% and specificity of 99.9%, the cheaper HLA-B*27 tag-SNP assay (rs116488202) could offer a less expensive yet rigorous testing option. Specifically, the HLA-B*27 tag-SNP assay could be ordered as a first-line screening test for patients … Address correspondence to Dr. P. Rahman, Professor of Medicine and Rheumatology, Memorial University, 154 LeMarchant Road, St. John’s, Newfoundland A1C 5B8, Canada. E-mail: prahman{at}mun.ca.
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".