External Quality Assessment of <i>HLA-B*5701</i> Reporting: An International Multicentre Survey
Bibliographic record
Abstract
OBJECTIVES: HLA-B*5701 strongly predicts abacavir hypersensitivity (HSR), but implementation of effective routine screening into clinical practice requires testing be practical and accurate. We tested the proficiency of HLA-B*5701 typing among laboratories using sequence-specific primer PCR. DESIGN AND METHODS: DNA panels (1 and 2) were distributed to seven laboratories (A to G) for blinded typing of the HLA-B*5701 allele. Panel 1 (n = 10 samples; n = 7 laboratories) included 3 positives and other closely related B17 subtypes (B*5702, B*5703, B*5704 and B*5801). Panel 2 (n = 96 samples; n = 4 laboratories) included 36 positives among a broad spectrum of other B alleles. Two laboratories (A and B) also submitted 96 routine samples, typed by the same methodology, to the reference centre for additional analysis by sequence-based typing. RESULTS: All laboratories correctly typed panel 1 for HLA-B*5701 carriage. Laboratories A, B and C identified HLA-B*5701 alleles in panel 2 with 100% sensitivity and 100% specificity. Laboratory D reported one false negative, reportedly due to a sampling error. The results obtained for routine samples typed by laboratories A and B and those generated by the reference laboratory using sequencing were fully concordant. CONCLUSIONS: Detection of HLA-B*5701 alleles among laboratories was 100% specific and 99.4% sensitive, indicating that participating HIV testing laboratories were currently offering effective primary screening to identify individuals at high risk of abacavir HSR. Accurate reporting of HLA-B*5701 status is critical for the safe administration of this drug and participation in quality assurance programmes by all sites who report HLA-B*5701 status should be promoted.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".