Analysis of cardiovascular disease and kidney outcomes in multidisciplinary chronic kidney disease clinics: complex disease requires complex care models
Bibliographic record
Abstract
PURPOSE OF REVIEW: Chronic kidney disease is recognized as being highly prevalent in the population, and associated with morbidity and mortality relative to the general population. The complexity of patients and the multiplicity of interventions required to maintain health has forced clinicians to develop different models of healthcare delivery. This publication reviews the current literature on specific interventions to reduce progression of chronic kidney disease and cardiovascular disease, and studies the examination of outcomes of patients exposed to different healthcare delivery models. Specifically we examine the rationale and outcomes of those seen in multidisciplinary clinics. RECENT FINDINGS: Current evidence supports the use of rennin-angiotensin system blockers, reduction of blood pressure and proteinuria and phosphate control. Additional less robust studies support the need for attention to anemia, hyperparathryoidism, and other more "kidney specific" risk factors. The attendance of identified chronic kidney disease patients at multidisciplinary clinics appears to improve survival once dialysis is started. Despite aggressive management, not all patients are able to meet clinical targets associated with improved outcomes. SUMMARY: The recognition of the complexity of chronic kidney disease care and the need to develop and test models of care in addition to the single interventions is a challenge for both researchers and clinicians. Current data support the use of multidisciplinary clinics in improving outcomes of referred patients. Future research will help to refine and define appropriate care models for this growing chronic kidney disease population.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".