HLA-B*58:01 genotyping prevalence and the association with allopurinol-induced severe cutaneous adverse reactions: a living systematic review and meta-analysis
Bibliographic record
Abstract
Evidence on the prevalence of HLA-B*58:01 genotyping and its association with allopurinol-induced severe cutaneous adverse reactions (SCARs) is lacking, especially in low-resource settings. We addressed these gaps by conducting comprehensive and race/ethnic origin-specific evaluations. We conducted a systematic search from inception to 31 December 2022 using databases (PubMed, Embase, and medRxiv), Google (for Vietnamese articles), and manual searching. We included original studies that investigated the association between HLA-B*58:01 genotyping and allopurinol-induced SCARs. We excluded studies on: (1) animals; (2) pharmacokinetics/pharmacodynamics; (3) genetic markers or genetic testing methods; (4) single group; and (5) cost-effectiveness of screening. Risk of bias was assessed using Newcastle–Ottawa Scale. We used random-effects model to report the summary estimates and 95% confidence interval (95% CI) in the meta-analysis. We included 13,719 patients from 24 case–control studies. The prevalences of HLA-B*58:01 genotyping (overall 5.8%; 95% CI 2.9% to 11.5%; I 2 = 98%) varied by races (Asian [7.7%; 95% CI 3.4% to 16.8%; I 2 = 98%] and White in Eastern/Western Europe [2.3%; 95% CI 1.2% to 4.3%; I 2 = 86%]) and ethnic origins (East and Central Asia [5.5%; 95% CI 1.5% to 17.8%; I 2 = 98%] and South and Southeast Asia [12.9%; 95% CI 9.5% to 17.3%; I 2 = 79%]). HLA-B*58:01 genotyping was associated with substantially increasing risk of allopurinol-induced SCARs (odds ratio 117.6; 95% CI 70.3 to 196.8; I 2 = 45%) regardless of the subgroups. We found a higher prevalence of HLA-B*58:01 genotyping in some Asian populations compared with the Whites. There is evidence to confirm a strong association between this allele and allopurinol-induced SCARs.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".