Evolving Paradigm of Precision Medicine in Cardiovascular Disease.
Bibliographic record
Abstract
In the year 1892, Sir William Osler, the legendary Canadian physician and one of the four founding professors of Johns Hopkins Hospital, said “If it were not for the great variability among individuals, medicine might as well be a science and not an art”. It is this heterogeneity among patients with seemingly homogenous medical conditions, that form the basis of what we today refer to as precision medicine. The fundamental of precision medicine is based on the tenets of ‘The right drug for the right patient at the right time’. Personalized or precision medicine found immense popularity in oncology. With the completion of Human Genome Project and the advent of genomics, big data and artificial intelligence, 21st century saw rapid progress of precision medicine in predicting, diagnosing and treating cancer. However, the same has not happened to cardiovascular diseases, the biggest killer of humanity. In this review article, we aim to address the concepts, components, outcomes and applications of precision medicine in general, and to review the evolving paradigm of how precision medicine is shaping the management of cardiovascular diseases. We delve deep into the aspects of risk prediction, preventative measures, and targeted therapeutic approaches for cardiovascular diseases. We also look at the recent trends and current applications of precision medicine in this area, the problems they solve and the challenges they possess, and what is in store for the future. Finally, we review the application of artificial intelligence specific to cardiovascular diseases, and the role of precision medicine in interventional cardiology.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".