Effects of flaxseed consumption on systemic inflammation and serum lipid profile in hemodialysis patients with lipid abnormalities
Bibliographic record
Abstract
Inflammation and lipid abnormalities are two important risk factors for cardiovascular disease in hemodialysis (HD) patients. The present study was designed to investigate the effects of flaxseed consumption on systemic inflammation and serum lipid profile in HD patients with lipid abnormalities. This was an unblinded, randomized clinical trial. Thirty HD patients with dyslipidemia (triglyceride >200 mg/dL and/or high-density lipoprotein-cholesterol (HDL-C) <40 mg/dL) were randomly assigned to either a flaxseed or control group. Patients in the flaxseed group received 40 g/day ground flaxseed for 8 weeks, whereas patients in the control group received their usual diet, without any flaxseed. At baseline and at the end of week 8, 7 mL of blood was collected after a 12- to 14-hour fast and serum concentrations of triglyceride, total cholesterol, low-density lipoprotein-cholesterol (LDL-C), HDL-C, and C-reactive protein (CRP) were measured. Serum concentrations of triglyceride (P < 0.01), total cholesterol (P < 0.01), LDL-C (P < 0.01), and CRP (P < 0.05) decreased significantly in the flaxseed group at the end of week 8 compared with baseline, whereas serum HDL-C showed a significant increase (P < 0.01). These changes in the flaxseed group were significant in comparison with the control group. The study indicates that flaxseed consumption improves lipid abnormalities and reduces systemic inflammation in HD patients with lipid abnormalities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".