Systematic review on the association of HLA-B*5801 and allopurinol-induced severe cutaneous adverse reactions
Bibliographic record
Abstract
Background: Studies have suggested a possible association between HLA-B*5801 and allopurinol induced Severe Cutaneous Adverse Reactions such as Stevens-Johnston Syndrome and Toxic Epidermal Necrolysis, which is fatal. \n Objectives: This systematic review aims to investigate the correlation between HLA-B*5801 and allopurinol-induced Severe Cutaneous Adverse Reactions, and whether gene-testing prior to allopurinol prescription might be a possible alternative. \n Methods: A comprehensive literature search was performed in MEDLINE and EMBASE from 2010 January to 2015 December. Only studies using allopurinol as the de novo drug and those investigating the association between HLA-B*5801 and allopurinol-induced Severe Cutaneous Adverse Reactions were included. Comparison were done between allopurinol induced Severe Cutaneous Adverse Reactions Case group with allopurinol tolerant control group and/or population control group. Systematic analysis was performed to examine the intervention and outcome. Newcastle-Ottawa Scale was used to evaluate the quality of individual studies. \nResults: A total of 4 published studies were identified. Comparison of association between case and allopurinol tolerant control group were found in Han Chinese and Hong Kong, while comparison of association between case and control population were found in Portuguese and Japanese. The presence of HLA-B*5801 with allopurinol induced SCAR was statistically significant p<0.05 in all the four individual studies. \nConclusion: This systematic review concludes that association of HLA-B*5801 and allopurinol induced Severe Cutaneous Adverse Reactions is statistically significant among different ethnic groups in all the four studies, prior gene test before allopurinol administration is recommended.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".