Epicardial adipose tissue volume increase in hemodialysis patients treated with sevelamer or calcium-based phosphate binders: a substudy of the Renagel in new dialysis trial
Bibliographic record
Abstract
BACKGROUND: In the general population and in hemodialysis patients epicardial adipose tissue (EAT) has been associated with increased mortality and cardiovascular events. Weight loss and lipid lowering therapies reduced EAT in the general population. It is unknown whether sevelamer, a phosphate (Pi) binder that lowers cholesterol and reduces inflammation in dialysis patients also affects EAT progression. METHODS: Post-hoc analysis of a randomized trial of sevelamer (SVL) versus calcium-based Pi binders (CPiB) in incident hemodialysis patients. EAT was measured on cardiac computed tomography scans performed at enrollment, 6, 12 and 18 months from baseline. RESULTS: Of 109 patients, 54 received SVL and 55 CPiB; the median LDL change was -16.4 % (IQR: -67.5, 142.3 %) and 12.1 % (IQR: -51.9, 193.8 %) with SVL and CPiB respectively (p < 0.001). At baseline EAT correlated significantly with gender, body mass index and total coronary artery calcium score (all p < 0.02). At the end of follow-up, EAT progressed significantly from baseline in the CPiB treated patients but not in the SVL treated patients [median increase 9.1 % (p = 0.005) vs 3.9 % (p = 0.25)]. However, there was no significant difference in the degree of progression between treatment groups (p = 0.34). There was no correlation between LDL or CRP change and EAT change. There were insufficient events in either arm to assess the impact of EAT change on mortality. CONCLUSION: EAT progression from baseline was significantly smaller with SVL than with CPiB, although the difference between treatments was not statistically significant, probably due to the small sample size. Change in serum lipids and markers of inflammation did not predict EAT progression.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".