Systematic Review of Thiopurine Methyltransferase Genotype and Enzymatic Testing Strategies
Bibliographic record
Abstract
BACKGROUND: An increased understanding of the genetic basis of disease creates a demand for personalized medicine and more accurate testing for diagnosis and treatment. Thiopurine methyltransferase (TPMT) plays an important role in the metabolism of thiopurine drugs used in pediatric leukemia, rheumatoid arthritis, and inflammatory bowel disease. The objective was to review the literature systematically to ascertain the performance characteristics of current genotype and enzymatic testing technologies for TPMT. METHODS: A systematic review of the literature was conducted to describe TPMT testing technologies. Eligible studies evaluated either a TPMT genotype or TPMT phenotype technology in comparison to a reference standard and expressed results in terms of sensitivity and specificity or positive/negative predictive value. The laboratory technique was recorded, and the quality of the identified studies was assessed using a modified Critical Appraisal Skills Program tool. RESULTS: Seventeen studies were reviewed. The sensitivity and specificity of the genotype test ranged from 55% to 100% and from 94% to 100%, respectively. The sensitivity and specificity of the phenotype test ranged from 92% to 100% and from 86% to 98%, respectively. A variety of laboratory techniques were employed. Reviewed studies were of low methodological quality. CONCLUSIONS: The systematic review of TPMT test strategies found that available technologies demonstrated high values for sensitivity and specificity, however, there was a lack of a single gold standard and most studies were of poor quality. Disregard for study sample size and confounding factors such as concurrent medications and blood transfusions were the main contributors to low quality. There were also inconsistencies in the selection of a reference standard which complicated the interpretation of the findings.
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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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".