Cardiovascular Magnetic Resonance Imaging
Bibliographic record
Abstract
The era of modern oncology incorporates an ever-evolving personalized approach to hematological malignancies and solid tumors. As a result, patient survival rates have, in part, substantially improved, depending on the specific type of underlying malignancy. However, systemic therapies may come along with potential cardiotoxic effects resulting in heart failure with increased morbidity and mortality. Ultimately, patients may survive their malignancy but die as a result of cancer treatment. Cardiovascular magnetic resonance imaging has long been in use for the assessment of function and tissue characteristics in patients with various nonischemic cardiac diseases. Besides an introductory overview on the general definition of cardiotoxicity including potential underlying mechanisms, this review provides insight into the application of various cardiovascular magnetic resonance imaging techniques in the setting of cancer therapy-related cardiac and vascular toxicity. Early identification of cardiotoxic effects may allow for on-time therapy adjustment and/or cardioprotective measures to avoid subsequent long-term heart failure with increased mortality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".