Bibliographic record
Abstract
Pharmacogenomics [PGx] is a part of individualized treatment or the personalized treatment regimen on the basis of intra and inter individual variability of population parameters such as genetics, environmental factors, diet, lifestyle and comorbidities etc. towards drug response. Which enables tailoring the therapy for better treatment efficacy and reduced toxicity issues especially with Narrow therapeutic indexed drugs. Nowadays the PGx is widely used across the world, where Human genome project and its completion enabled the understanding and use the of the PGx parameters from drug molecule designing to till elimination of the drug from the body and for clinical judgement towards precision Pharmacotherapy. Recently PGx was also included in the accreditation standards of both the Canadian Council for Accreditation of Pharmacy Programs and the Accreditation Council for Pharmacy Education, USA as well as in the American Society of Health-System Pharmacists. The PGx plays a major role in clinical Pharmacy in terms of optimizing the dose, identification of potential drug-drug interactions, minimizing the toxicity and understanding the pharmacokinetics and pharmacodynamics of the drugs such as Dihydropyrimidine dehydrogenase (DPYD) gene for 5-Fluorouracil (5-FU), CYP2C19-clopidogrel, CYP2C9/VKORC1-warfarin and CYP2D6-opioids and mainly cost effectiveness and cost saving of PGx testing which are listed in the Pharmacogenomics knowledge database (PharmGKB) and Clinical Pharmacogenetics Implementation Consortium (CPIC) database. How ever the barriers to implementation includes the knowledge gaps, cost considerations, and a lack of guidelines and evidence. Therefore, PGx implementation in clinical pharmacy poses a positive attitude towards evidence-based medicine and decision making in all scenarios where the drugs optimization highlighting the importance of Clinical Pharmacologist in Healthcare Professional team in future days.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.030 | 0.012 |
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".