Current and novel imaging techniques to evaluate myocardial dysfunction during hemodialysis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients on hemodialysis have significantly higher rates of cardiovascular mortality resulting from a multitude of myocardial dysfunctions. Current imaging modalities allow independent assessment of cardiac morphology, contractile function, coronary arteries and cardiac perfusion. Techniques such as cardiac computed tomography (CT) imaging have been available for some time, but have not yet had widespread adoption because of technical limitations related to cardiac motion, radiation exposure and safety of contrast agents in kidney disease. RECENT FINDINGS: Novel dynamic contrast-enhanced (DCE) CT imaging can be used to acquire high-resolution cardiac images, which simultaneously allow the assessment of coronary arteries and the quantitative measurement of myocardial perfusion. The advancement of recent CT scanners and cardiac protocols have allowed noninvasive imaging of the whole heart in a single imaging session with minimal cardiac motion artefact and exposure to radiation. SUMMARY: DCE-CT imaging in clinical practice would allow comprehensive evaluation of the structure, function, and hemodynamics of the heart in a short, well tolerated scanning session. It is an imaging tool enabling the study of myocardial dysfunction in dialysis patients, who have greater cardiovascular risk than nonrenal cardiovascular disease populations, both at rest and under cardiac stress associated with hemodialysis itself.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".