Magnetic Resonance Imaging of Cardiovascular Manifestations Following <scp>COVID</scp>‐19
Bibliographic record
Abstract
Globally, over 650 million people have had COVID-19 due to infection with the SARS-Cov-2 virus. Cardiac complications in the acute infectious and early recovery phase were recognized early in the pandemic, including myocardial injury and inflammation. With a decrease in the number of acute COVID-19 related deaths, there has been increased interest in postacute sequela of COVID-19 (PASC) and other longer-term cardiovascular complications. A proportion of patients recovered from COVID-19 have persistent cardiac symptoms and are at risk of cardiovascular disease. Cardiovascular imaging, including MRI, plays an important role in the detection of cardiovascular manifestations of COVID-19 in both the acute and longer-term phases after COVID-19. The purpose of this review is to highlight the role of cardiovascular imaging in the diagnosis and risk stratification of patients with acute and chronic cardiovascular manifestations of COVID-19 with a focus on cardiac MRI. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 3.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".