The Use of Precision Epigenetic Methods for the Diagnosis and Care of Stable Coronary Heart Disease Reduces Healthcare Costs
Bibliographic record
Abstract
INTRODUCTION: The cost of secondary prevention of coronary heart disease (CHD) is continuing to increase, with a substantial portion of this acceleration being driven by the expense of confirmatory diagnostic testing. Conceivably, newly developed precision epigenetic technologies could drive down these costs. However, at the current time, their impact on overall expense for CHD care is poorly understood. We hypothesized that the use of a newly developed, highly sensitive, and specific epigenetic test, PrecisionCHD, could decrease the costs of secondary prevention. METHODS: To test this hypothesis, we constructed a budget impact analysis using a cost calculation model that examined the effects of substituting PrecisionCHD for conventional CHD diagnostic tests on the expenses of the initial evaluation and first year of care of stable CHD using a 1-year time horizon with no discounting. RESULTS: The model projected that for a commercial insurer with one million members, full adoption of PrecisionCHD as the primary method of initial CHD assessment would save approximately $113.6 million dollars in the initial year. CONCLUSION: These analyses support the use of precision epigenetic methods as part of the initial diagnosis and care of stable CHD and can meaningfully reduce cost. Real-world pilots to test the reliability of these analyses are indicated.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".