Bibliographic record
Abstract
Heart disease is a major cause of morbidity and mortality among patients with renal failure. Premature atherosclerotic coronary heart disease is driven by multiple risk factors, including dyslipidemia and oxidative stress. In the nondialysis population, there is overwhelming evidence that treatment of dyslipidemia can significantly improve cardiovascular outcomes. Accumulating data indicate that dialysis patients have atherogenic lipid abnormalities. Although LDL cholesterol (LDL-C) levels in patients who undergo hemodialysis are normal or near normal, increased oxidized LDL-C, triglycerides, and lipoprotein (a) [Lp(a)]; decreased HDL cholesterol (HDL-C); and triglyceride-rich VLDL have been noted. Patients who receive peritoneal dialysis have a more atherogenic lipid profile with increased LDL-C, apolipoprotein B, oxidized LDL-C, triglycerides, and Lp(a) and decreased HDL-C. Furthermore, the LDL particles of peritoneal dialysis patients are small and dense. However, there is a dearth of information regarding the goals, efficacy, and safety of dyslipidemia treatment among dialysis patients. Given the strong evidence of risk reduction and the benefits of lipid-lowering treatment in the nondialysis population, the emerging consensus is that dialysis patients should be treated aggressively for dyslipidemia to an LDL-C goal below 100 mg/dl. Although physicians and patients may be reluctant to add medications because of concerns about polypharmacy, potential decreased compliance, and increased cost, the use of agents such as sevelamer that can serve multiple functions, including phosphate control, lipid lowering (decreased LDL-C and total cholesterol), and anti-inflammatory effects (decreased high-sensitivity C-reactive protein), should be explored and considered for patients who would benefit from such treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".