Navigating the Crossroads of Coronary Artery Disease and Heart Failure
Bibliographic record
Abstract
Chronic heart failure (HF) affects 5 million patients in theUnited States and is responsible for 1 million hospi-talizations and 300 000 deaths annually.1 The total annual costs associated with this disorder have been estimated to exceed $40 billion.1,2 Chronic HF is the only category of cardiovascular diseases for which the prevalence, incidence, hospitalization rate, total burden of mortality, and costs have increased in the past 25 years.1,2 Fueling this epidemic is the increasing number of elderly patients developing impaired left ventricular (LV) function as a manifestation of chronic coronary artery disease (CAD).1,2 With the aging of the population and decline in mortality of other forms of cardio-vascular diseases, it is likely that the incidence of HF and its impact on public health will continue to increase.1–3 CAD and HF: Epidemiology and Prognosis In the past 3 decades, considerable attention has focused on
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".