Opportunities for the Cardiovascular Community in the Precision Medicine Initiative
Bibliographic record
Abstract
The Precision Medicine Initiative recently announced by President Barack Obama seeks to move the field of precision medicine more rapidly into clinical care. Precision medicine revolves around the concept of integrating individual-level data including genomics, biomarkers, lifestyle and other environmental factors, wearable device physiological data, and information from electronic health records to ultimately provide better clinical care to individual patients. The Precision Medicine Initiative as currently structured will primarily fund efforts in cancer genomics with longer-term goals of advancing precision medicine to all areas of health, and will be supported through creation of a 1 million person cohort study across the United States. This focused effort on precision medicine provides scientists, clinicians, and patients within the cardiovascular community an opportunity to work together boldly to advance clinical care; the community needs to be aware and engaged in the process as it progresses. This article provides a framework for potential involvement of the cardiovascular community in the Precision Medicine Initiative, while highlighting significant challenges for its successful implementation.
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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 | 0.149 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.034 | 0.026 |
| Open science | 0.005 | 0.049 |
| Research integrity | 0.047 | 0.062 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".