Gazing through the Crystal Ball of Science—Cardiovascular Disease in 2100
Bibliographic record
Abstract
Recently, we had the opportunity to review the progress that has been made in the field of cardiovascular disease over the past century in The FASEB Journal and, based on those thoughts, in this article we predict what may transpire in this ‘century of biology’. Although it is true that ‘the best way to predict the future is to invent it’, we gaze through the prism of modern biomolecular science for a vision of a possible future and see cardiology practice that is transformed. In the second half of the 20th century, we developed a more fundamental understanding of atherosclerotic vascular disorders and invented life-saving therapeutics. We saw a similar development of mechanism-based pharmacotherapy to address heart failure, primarily through agents that antagonize the excessive concentration of circulating neurohumoral agents. Now we are in the midst of the device era, from stents to cardiac resynchronization therapy to transcatheter valves. The next wave of treatments will build on an increasingly sophisticated understanding of the molecular determinants of cardiovascular disorders and engineering feats that are barely perceptible now. Genomic profiling, molecular prescriptions for prevention and personalized therapeutics, regenerative medicine and the new field of cardiovascular tissue bioengineering will transform cardiovascular medicine. If the human species can survive threats of our own doing, such as the related epidemics of obesity and diabetes, by the turn of the next century, treatment of cardiovascular disease will not resemble the present in almost any way.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".