EFFECTIVENESS OF EZETIMIBE IN TREATMENT OF HYPERCHOLESTEROLEMIA IN RENAL TRANSPLANT RECIPIENTS.
Bibliographic record
Abstract
P298 Aims: Despite the evidence that reduction of low-density lipoprotein cholesterol (LDL-C) effectively reduces cardiovascular morbidity and mortality, many kidney transplant recipients are unable to achieve the recommended LDL target of <2.6mmol/L, even with statin therapy. In addition some patients are intolerant of current lipid lowering therapy. Ezetimibe a newer cholesterol absorption inhibitor can be used as monotherapy or in combination with statins. Methods: We evaluated the short term efficacy of ezetimibe, 10 mg daily dose, in 20 stable outpatients by measuring fasting lipids, liver function tests, and immunosuppressive drug levels pre and 4 weeks post therapy. Results: Patients were 48±12.2 years old and their weight was 82.8±25.3 kg. Nine were female, 8 had diabetes mellitus, and 13 on statin therapy. Primary immunosuppression was cyclosporine (9), tacrolimus (6), and rapamycin (5). Table 1 shows the pre and post dose effect after four weeks of therapy.FigureThere was a significant reduction in total cholesterol and LDL-C, without any adverse effect on TG or HDL. Patients on a statin had a more pronounced drop (37%±12%) in LDL compared to those on no statin (17%±6%, p=0.036). There was no significant change in the kidney function, liver enzymes or levels of immunosuppresive medications. The medication was well tolerated in all patients. Conclusions: Ezetimibe improves lipid control in the kidney transplant population. Further study will be needed to show whether cardiovascular events are reduced or whether the drug has more subtle toxicity.
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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) | 0.001 | 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 | 0.001 |
| 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".