Personalized Medicine: An Idea Whose Time is Approaching
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The field of Cardiology has seen major advancements in the past 50 years, including the advent of cardiac catheterization, bypass surgery, angioplasty, defibrillators and ablation. Basic research and clinical trials have defined the efficacy and safety of a range of treatments such as statin therapy for coronary artery disease and ACE inhibitors for heart failure. As a result, this "evidence-based medicine" has lead to the development and widespread use of clinical guidelines that have helped reduce cardiac mortality by more than 50% over the past 30 years.The 21st century is poised to implement an even higher standard of evidence-based medicine - namely, therapy personalized to an individual's genetic variants. The gene is the most fundamental biological unit of the human body, responsible for generating and regulating proteins that perform the body's functions. Genes and their regulators determine which, when and how many proteins are synthesized in response to a host of signals, which makes genetics a major factor in many diseases. In fact, it is well recognized that more than 50% of one's predisposition to coronary artery disease is genetic. While coronary artery disease (CAD) is still the number-one killer in the Western world, the availability of the DNA sequence of human genes could mean its demise in the 21st century.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it