The Effect of Coronary Artery Status on Myocardial Perfusion Response to Hemodialysis
Bibliographic record
Abstract
Background: Hemodialysis (HD) is associated with repetitive ischemiareperfusion cardiac injury occurring during each treatment that accumulates with subsequent treatments. Conventional cardiovascular therapies effective in patients with atherosclerotic disease or myocardial infarction have been largely ineffective in treating HD-induced injuries. The objective of the present study was to use coronary CT angiography (CCTA) and intradialytic CT perfusion imaging to noninvasively evaluate the myocardial perfusion response during HD in patients with and without significant coronary artery stenosis. Methods: CCTA images were acquired prior to HD (baseline) on ten patients and assessed by an experienced radiologist for clinically significant stenoses. In addition, dynamic contrast-enhanced CT scans (Revolution CT, GE) were conducted at baseline, peak HD stress, and 30 mins post HD. The dynamic CT images were analyzed using the Johnson-Wilson-Lee tracer kinetic model to quantify global myocardial perfusion (MP) of the left ventricle. Results: Three patients were identified with clinically significant stenoses. In all patients, MP decreased from baseline to peak HD. However, MP response to HD was not significantly different between patients with clinically significant stenoses and those with no stenosis (Fig 1).Fig 1.: Mean global MP at baseline, peak HD stress, and 30 mins post HD in patients with no stenosis and with significant stenosis. Error bars represent standard error of the mean. Significance of ** and * In no stenosis group denote p <0.01 and p<0.05, repectively.Conclusions: Preliminary results indicate that the coronary artery status does not affect the myocardial perfusion response to HD. This suggests that the decrease in MP during HD is caused by the treatment itself, rather than by coronary artery stenosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".