Improving the Sensitivity and the Specificity of HLA-B27 Typing by Replacing Serological Tests with a TaqMan-PCR Assay
Bibliographic record
Abstract
Abstract HLA-B27 is a strong diagnostic biomarker as it is found in 90– 95% of ankylosing spondylitis. Routinely, the false-positive results generated by cross-reactivity of HLA-B27 monoclonal antibodies (mAbs) with some other type of HLA-B antigens are problematic. The present study aims at improving the sensitivity and specificity of typing for HLA-B27 by using a more accurate DNA assay. A real time sequence specific primer polymerase chain reaction (PCR-SSP) is performed by using MGB TaqMan oligoprobe and an ABI PRISM™ 7500 sequence detection system. The TaqMan PCR-SSP assay is compared with Immunophenotyping by flow cytometric antigen assay (BD HLA-B27-Kit, clone GS145.2). The results are finally confirmed by sequencing (ABI PRISM ® 3100). As summarised in the table, three methods are identical for clearly positive and negative results. There is 90% concordance for borderline negative results obtained by immunophenotyping with TaqMan PCR-SSC and/or sequencing. However, only 15.4% of borderline positives obtained by immunophenotyping are confirmed as true HLA-B27 positives by sequencing as well as TaqMan PCR-SSC. TaqMan PCR-SSP DNA assay completely correlates with sequencing (gold standard) with 100% PPV and NPV, compared to a PPV of 15% and a NPV of 98% for borderline results by immunophenotyping. Conclusion: when compared with flow cytometric antigen assay, typing HLA-B27 by TaqMan PCR-SSP clearly demonstrates an advantage to better establish the genotype of the patient. Specifically, there is a marked difference for ambitious positive results from immunophenotyping. Moreover, compared to immunophenotyping, there is no need of fresh samples, can test a larger number of samples concomitantly, and reduces technical time as well as cost. N = 52 Immunophenotyping TaqMan PCR-SSP Sequencing Immuno vs Sequencing TaqMan PCR-SSP vs Sequencing Positive 11 11 11 100% 100% Negative 17 17 17 100% 100% Positive (borderline by Immunophenotyping) 13 2 2 15% 100% Negative (borderline ) by Immunophenotyping) 10 9 9 98% 100%
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".