Thrombosis, Translational Medicine, and Biomarker Research: Moving the Needle
Bibliographic record
Abstract
Arterial and venous thromboembolism are leading causes of morbidity and death worldwide. Despite significant advances in the diagnosis, prognostication, and treatment of thrombotic diseases over the past 3 decades, the adoption of findings stemming from translational biomarker research in clinical practice remains limited. Biomarkers provide an opportunity to enhance our understanding of pathophysiological processes and optimize treatment strategies. They hold the promise of revolutionizing patient care. Still, this potential remains untapped, and several factors impede their use for near-patient applications. We sought to provide an overview of biomarker research in arterial and venous thromboembolic disease. We then aimed to discuss key barriers to the broader clinical implementation of biomarker research and highlight promising strategies to overcome them. We emphasize the merits of translational and implementation science to bridge the gaps from bench to bedside. Innovative trial design, data sharing, and collaborative efforts between academia and industry will be essential. Purposeful regression methodology using rational conceptual framework design, causal mediation analysis, and artificial intelligence might better leverage the use of observational data. Dedicated translational science training programs geared toward educating physicians on the appropriate measurement, interpretation, and integration of biomarker data in clinical practice should foster endorsement by frontline physicians. Finally, we make the case in support of a paradigm shift in cardiovascular medicine. Improved recognition of biomarker research and a greater emphasis on mechanistic evidence can better equip clinicians to deal with the uncertainty that defines the practice of thrombosis medicine.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".