Nuclear Cardiology Surrogate Biomarkers in Clinical Trials
Bibliographic record
Abstract
Nuclear cardiology offers a diverse range of imaging tools that provide valuable insights into myocardial perfusion, inflammation, metabolism, neuroregulation, thrombosis, and microcalcification. These techniques are crucial not only for diagnosing and managing cardiovascular conditions but also for gaining pathophysiologic insights. Surrogate biomarkers in nuclear cardiology, represented by detectable imaging changes, correlate with disease processes or therapeutic responses and can serve as endpoints in clinical trials when they demonstrate a clear link with these processes. By providing early indicators of therapeutic efficacy-often before clinical outcomes manifest-surrogate biomarkers can accelerate treatment development. This disease-focused review will highlight key nuclear cardiology surrogate biomarkers, emphasizing the importance of standardized imaging protocols and robust quantitative techniques to ensure accuracy and reproducibility. We will also explore the challenges to the broader adoption of imaging biomarkers, including the need for well-defined pathophysiologic correlations, greater data diversity in clinical research, and overcoming regulatory barriers. Addressing these challenges will improve the utility of imaging biomarkers in clinical trials, enabling more precise cardiovascular care through early diagnosis and therapeutic monitoring, ultimately accelerating the development of novel cardiovascular therapies.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".