Cardioprotection in the ESRD Population: How Do We Get There?
Bibliographic record
Abstract
Cardiovascular mortality for end-stage renal disease (ESRD) patients is about 30 times the risk in the general population. About 30% of ESRD patients have hyperlipidemia. The 1998 National Kidney Foundation Task Force on Cardiovascular Disease recommends implementation of effective measures to prevent and treat cardiovascular disease in this population. Our intent was to evaluate the extent of use of cardioprotective drugs in ESRD patients through a quality improvement project. Twenty-eight dialysis facilities throughout Ohio volunteered for this project. Data regarding use of angiotensin-converting enzyme inhibitors (ACE-I) and angiotensin receptor blockers (ARB) in heart failure, beta-blockers in myocardial infarction (MI), aspirin in coronary artery disease, and 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) were collected using chart abstraction for the period March through May 2000. The results were compared to Ohio hospital discharges from July through September 2000. This latter population was comprised of non--ESRD patients. Dialysis facilities were visited and interviews were conducted with staff members. Information was gathered regarding facility infrastructure, quality improvement process, and existing protocols. 27% of ESRD patients with a history of heart failure were on ACE-I, compared to 75.7% of non-ESRD patients. 34.8% of ESRD patients with a previous MI were taking beta-blockers, compared with 68.0% of non-ESRD patients with a prior MI. Aspirin use in ESRD patients with a previous MI was 52.8%, compared to 88% in non-ESRD patients with a prior MI. 17.3% of ESRD patients were on statins. Hyperlipidemia is found in 30% to 50% of ESRD patients. The use of cardioprotective drugs in the Medicare ESRD patient is lower than in the Medicare non-dialysis counterpart. Reasons for this are related to fragmentation of health care arising from communication and infrastructure issues. Until these issues are addressed and resolved, efforts at initiation of cardioprotective strategies will be slowed.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".