Multiparametric cardiac magnetic resonance imaging of the heart in people with spinal cord injury
Bibliographic record
Abstract
Cardiovascular disease is the foremost cause of death for individuals with spinal cord injuries and is a patient priority in this population. To reduce cardiovascular disease, health care costs, and address key patient‐centered priorities, we must understand how and why heart health deteriorates after high‐level spinal cord injuries. Using high‐resolution cardiac magnetic resonance imaging and analysis approaches (ie., 4D strain analysis, 4D flow imaging, late gadolinium enhancement), our objective was to evaluate the impact of high‐level spinal cord injury on cardiac structure and function. Cardiac dimensions including chamber wall thickness were reduced in people with cervical spinal cord injury compared to matched controls and fibrosis was more apparent. Furthermore, we observed changes in global markers of strain in the heart including both longitudinal and circumferential strain after spinal cord injury. Cardiac magnetic resonance imaging revealed deficits in structure and function of the left ventricle after SCI. The next steps are to establish the risk factors leading to abnormal hearts after spinal cord injury, and develop strategies for prevention and treatment. Support or Funding Information Natural Sciences and Engineering Research Council of Canada, Canadian Institutes of Health Research, Stephenson Cardiac Imaging Centre, University of Calgary, Libin Cardiovascular Institute, Hotchkiss Brain Institute, Campus Alberta Neuroscience, Clinical Neurosciences Pilot Research Fund Program, Compute Canada, Rick Hansen Institute, Wings for Life Foundation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".