Improvement in Lipid Profile by Nocturnal Hemodialysis in Patients with End-Stage Renal Disease
Bibliographic record
Abstract
Dyslipidemia is associated with uremia and an increased risk of cardiovascular disease. The uremic dyslipidemia syndrome is characterized by an abnormal lipoprotein profile that results in (1) an elevation of triglyceride (TG) rich lipoproteins, very low density lipoprotein (VLDL), and intermediate density lipoprotein (IDL); (2) a reduction in high density lipoprotein (HDL) levels; and (3) a higher fraction of atherogenic, small dense low density lipoprotein (LDL). Nocturnal hemodialysis (NHD) is a home based renal replacement therapy that provides better control of uremia than conventional hemodialysis (CHD) and that may improve dyslipidemia. To test this hypothesis, we conducted a prospective cohort study of 11 patients with end-stage renal disease (ESRD) (age 38+/-3 years [mean+/-SEMI) before and after conversion from CHD to NHD. Weight, blood pressure (BP), serum hemoglobin (Hb), phosphate (PO4), and albumin (Alb) were assessed at baseline and at 3 months after conversion to NHD. Dialysis dose on CHD and NHD was assessed using equilibrated Kt/V (eKt/V). A 12 hour fasting lipid profile (total cholesterol [TC], TG, HDL, LDL, HDL/TC) was obtained once while on CHD and at 3 months after conversion to NHD. After conversion from CHD to NHD, eKt/V per session increased significantly (from 1.13+/-0.05 to 2.10+/-0.07; p < 0.05). TG level decreased significantly (from 2.05+/-0.30 to 1.01+/-0.14 mmol/L; p < 0.001), and HDL level increased significantly (from 1.17+/-0.13 to 1.65+/-0.14 mmol/L; p < 0.001). HDL/TC also increased significantly (from 0.26+/-0.03 to 0.35+/-0.02; p < 0.001). TC and LDL levels were unchanged. HDL levels increased and TG levels decreased in all patients. There was no difference in weight, Hb, and Alb. Systolic BP and PO4 were significantly lower, and there was a trend toward a reduction in cardiovascular medications. The mechanism for the improvement in lipid profile requires further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".