Editorial: Novel methods to advance diagnostic and treatment value of medical imaging for cardiovascular disease
Bibliographic record
Abstract
Editorial on the Research Topic Novel methods to advance diagnostic and treatment value of medical imaging for cardiovascular diseaseThe use of medical imaging has substantially increased over the past decade, thanks to the technological advancements evident from the dramatic improvement in the sensitivity and spatial resolution of imaging modalities.Cardiovascular imaging has been at a crossroads regarding technological advances, with a shift in focus from single-modality diagnosis to an integrated multimodality approach that can provide comprehensive assessments of morphology, pathophysiology, and disease biology to stratify the patient risk and guide therapies.The wide inter-subject variability in cardiovascular anatomy and pathophysiology urges the design of personalized patient management, which can highly benefit from clinical imaging technologies.The remarkable advances in medical imaging have sparked the development of new image processing algorithms and image-based simulation tools.In addition to providing comprehensive diagnostic information, some tools can even predict intervention outcomes, thereby enabling personalized intervention planning.This Research Topic, Novel Methods to Advance Diagnostic and Treatment Value of Medical Imaging for Cardiovascular Disease, focuses on tools that augment the power of medical imaging to provide detailed quantification of cardiovascular disease.Here, we present a Research Topic of 21 research articles that provide the
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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".