Association of HLA-B*5701 Genotypes and Abacavir-Induced Hypersensitivity Reaction: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVES: This study aimed to systematically review and quantitatively synthesize the association between HLA-B*5701 and abacavir-induced hypersensitivity reaction (ABC-HSR). METHODS: We searched for studies that investigated the association between HLA-B genotype and ABC-HSR and provided information about the frequency of carriers of HLA-B genotypes among cases and controls. We then performed a meta-analysis with a random-effects model to pool the data and to investigate the sources of heterogeneity. RESULTS: From 1,026 articles identified, ten studies were included. Five using clinical manifestation as their diagnostic criteria, 409 and 1,883 subjects were included as cases and controls. Overall OR was 23.6 (95% CI = 15.4 - 36.3). Whereas, the another five studies using confirmed immunologic test as their diagnostic criteria, 110 and 1,968 subjects were included as cases and controls, respectively. The association of ABC-HSR was strong in this populations with HLA-B*5701. Overall OR was 1,056.2 (95% CI = 345.0 - 3,233.3). CONCLUSIONS: Using meta-analysis technique, the association between HLA-B*5701 and ABC-HSR is strong in the studies using immunologic confirmation to identify ABC-HSR. These results support the US FDA recommendations for screening HLA-B*5701 allele before initiating abacavir therapy.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.011 | 0.010 |
| 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".