Association of coronary artery calcium score and vascular dysfunction in long‐term hemodialysis patients
Bibliographic record
Abstract
Long-term hemodialysis patients are prone to an exceptionally high burden of cardiovascular disease and mortality. The novel temperature-based technology of digital thermal monitoring (DTM) of vascular reactivity appears associated with the severity of coronary artery disease in asymptomatic population. We hypothesized that in hemodialysis patients, the DTM and coronary artery calcium (CAC) score have a gradient association that follows that of subjects without kidney disease. We examined the cross-sectional DTM-CAC associations in a group of long-term hemodialysis patients, and their 1:1 matched normal counterpart. Area under the curve for temperature (TMP-AUC), the surrogate of the DTM index of vascular function, was assessed after a 5-minute arm-cuff reactive hyperemia test. Coronary calcium score was measured via electron beam computed tomography or multidetector computed tomography scan. We studied 105 randomly recruited hemodialysis patients (age: 58 ± 13 years, 47% men) and 105 age- and gender-matched controls. In hemodialysis patients vs. controls, TMP-AUC was significantly worse (114 ± 72 vs. 143 ± 80, P = 0.001) and CAC score was higher (525 ± 425 vs. 240 ± 332, P < 0.001). Hemodialysis patients were 14 times more likely to have CAC score >1000 as compared with controls. After adjustment for known confounders, the relative risk for case vs. control for each standard deviation decrease in TMP-AUC was 1.46 (95% confidence interval: 1.12-1.93, P = 0.007). Vascular reactivity measured via the novel DTM technology is incrementally worse across CAC scores in hemodialysis patients, in whom both measures are even worse than their age- and gender-matched controls. The DTM technology may offer a convenient and radiation-free approach to risk-stratify hemodialysis patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".