Lipoprotein (a) in Chronic Renal Failure: Effect of Maintenance Hemodialysis
Bibliographic record
Abstract
BACKGROUND: Coronary artery disease accounts for significant morbidity and mortality in patients with chronic kidney disease (CKD). Besides the higher prevalence of traditional risk factors, several uremia-related factors may play a role in accelerated atherosclerosis, such as elevated levels of lipoprotein (a) (Lp(a)). The effect of maintenance hemodialysis (MHD) on Lp(a) levels is not well understood. The present work was carried out to study the Lp(a) levels in Stage 4 and Stage 5 CKD patients as well as the effect of MHD on Lp(a) levels in patients with Stage 5 CKD. METHODS: The study subjects included 15 patients with Stage 4 CKD, 15 patients with Stage 5 CKD, and 15 age- and sex-matched healthy controls. Plasma Lp(a) was measured by ELISA in all the subjects at the time of entry into the study and after 4 weeks of MHD in patients with Stage 5 CKD. Patients on MHD were dialyzed two to three times weekly for 4 hr during each session. RESULTS: Mean Lp(a) levels were significantly higher in patients with CKD than in control patients. In patients with Stage 4 CKD, the Lp(a) level was 34.0 +/- 19.5 mg/dL, whereas in Stage 5 CKD the level was 49.0 +/- 30.9 and in healthy controls it was 22.2 +/- 16.4. In patients with Stage 5 CKD, 4 weeks of MHD led to a significant fall in Lp(a) levels by 23.6% (P < 0.001). CONCLUSIONS: The results of this study show that increases in Lp(a) levels start early during the course of CKD and become more pronounced with increased severity of disease. Initiation of MHD lowers Lp(a) levels and may have a long-term beneficial effect on cardiovascular morbidity and mortality.
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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) | 0.000 | 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".