The Pharmacogenomic Era in Asia: Potential Roles and Challenges for Asian Pharmacists
Bibliographic record
Abstract
Personalized medicine through Pharmacogenomics: choosing the right drug, and the right dose, for the right patients based on patient’s genetic makeup-is gradually being realised in Western countries. Yet, the practice of pharmacogenomics in Asian countries lags behind that of the West, but the medical needs for pharmacogenomics are expected to surge as better patient care is demanded in Asia. As next-generation sequencing technology advances quickly, previous technical challenges for performing pharmacogenomic studies or practices in Asia have been mostly resolved. What is lacking in Asia is an effective model of community-wide pharmacogenomics. On the delivery front, pharmacists, the drug and dosing professionals, can potentially be the main healthcare providers for pharmacogenomic services in Asia. The first large “Genomics for Precision Drug Therapy in the Community Pharmacy” in Canada, which is close to its completion, has successfully identified community pharmacists as key contact professionals for smooth facilitation and implementation of pharmacogenomics for personalized medication. It is anticipated that Asian pharmacists, with appropriate training, can have the capacity to provide expert pharmacogenomic supports for both physicians and patients in Asia.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".