Lipoprotein (a) in CRF: Effect of Maintenance Hemodialysis
Bibliographic record
Abstract
Coronary artery disease is a major cause of morbidity and mortality in patients with chronic renal failure (CRF). Besides the higher prevalence of traditional risk factors, several uremia-related factors may play a role. Although lipoprotein (a)[Lp (a)] is largely determined by genetic factors, there is some evidence to suggest elevated levels of Lp (a) in patients with CRF. However, the effect of maintenance hemodialysis (MHD) on Lp (a) levels has not been studied. Objective: To evaluate the profile of serum Lp (a) and carotid intima-media thickness (IMT) in patients with various grades of CRF and to study the effect of MHD on Lp (a) levels in patients with advanced CRF. Methods: The study group comprised of patients with mild to moderate CRF (n = 15) and advanced CRF (n = 15). Fifteen healthy controls were also enrolled. Serum Lp (a) level was measured and carotid doppler study was done in all cases. In patients with advanced CRF serum Lp (a) level was again measured after one month of MHD. Results: Serum Lp (a) showed a progressive rise with increase in severity of CRF. As against 20% controls, 60% of patients with mild-moderate CRF and 73% of patients with advanced CRF had Lp (a) levels more than 30 mg/dl. In patients with advanced CRF, repeat Lp (a) levels after 4 weeks of MHD showed a decline by 23.6% with a range of 2.5%–50%(p < 0.001)(Figure) However there was no significant difference in the carotid IMT amongst the three groups. Conclusion: Patients with CRF have significant elevation of Lp (a) level. This lipid abnormality starts early during the course of CRF and shows progressive worsening with increase in severity of CRF. In patients with advanced CRF, MHD results in significant decline in Lp (a) levels.
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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 | 0.001 |
| 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) | 0.000 | 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".