Pharmacogenomics Applications in Clinical Practice: Revolutionizing Patient Care
Bibliographic record
Abstract
Background: Personalised medicine through pharmacogenomics is revolutionalizing healthcare delivery by encouraging individualized therapy that takes into consideration an individual's genetic profile, environment and lifestyle. Pharmacogenomics is an aspect of pharmacy that studies the relationship between genetic profile and response to therapeutic agents. However, the application of the concepts of pharmacogenomics in healthcare helps in achieving more effective and safe responses from therapy. This study evaluates the application and benefits of pharmacogenomics in clinical practice based on evidence from current practices in various medical fields. Methods: In carrying out this review, PubMed database was the primary literature source and we analyzed and synthesized findings from the included literature thematically as it relates to pharmacogenomics applications, benefits and challenges as well as safety and ethical concerns. Results: Pharmacogenomics has been widely applied in various aspects of healthcare such as in dosing, choice of treatment, reducing and management of adverse reactions, individualization of therapy, optimizing efficacy of therapy. Despite its numerous applications, its adoption faces challenges such as limited clinical evidence, lack of specialized training among healthcare professionals, cost and complexity of genetic mapping as well as ethical concerns. Conclusion: With ongoing advances in genomic technologies, pharmacogenomics is becoming an integral aspect of individualization therapy in clinical practice and more widely applied in different healthcare sectors.
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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 | 0.039 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".