The AmpliChip: A Review of its Analytic and Clinical Validity and Clinical Utility
Bibliographic record
Abstract
BACKGROUND: In 2005, the FDA approved the Roche AmpliChip™ for clinical application. The AmpliChip is a microarray chip that has the capability to play an important role in clinical pharmacogenetics. OBJECTIVE: Because of the possible influence the AmpliChip may have on patient medication management, the purpose of the review is to address the available evidence for the AmpliChip's overall performance at three key levels: analytic validity (genotyping accuracy, and prediction of the phenotype from the genotype) and clinical utility. DATA SOURCES: We searched Medline, Embase and PubMed for studies of the AmpliChip. Limits were English language and 2005 (the year of FDA approval) and onwards, and we corresponded with authors for further papers of interest. RESULTS: 17 articles provided data for analysis in this review: 4 involving genotype accuracy, 7 involving genotype to phenotype prediction and 9 involving clinical utility. CONCLUSION: There is limited literature comparing AmpliChip results to gold standard tests and test-retest reliability when assessing genotype accuracy. Also, there is limited literature on the accuracy of AmpliChip predictions of phenotypes from genotypes and minimal evidence with appropriately powered studies whether the AmpliChip genotype to phenotype predictions result in clinical benefit. At all three levels there is significant evidence that the AmpliChip has the potential to be a robust clinical tool. However, more and adequately powered studies are required to determine fully whether the AmpliChip is a clinically effective tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".