Canadian Chronic Kidney Disease Clinics: A National Survey of Structure, Function and Models of Care
Bibliographic record
Abstract
BACKGROUND: The goals of care for patients with chronic kidney disease (CKD) are to delay progression to end stage renal disease, reduce complications, and to ensure timely transition to dialysis or transplantation, while optimizing independence. Recent guidelines recommend that multidisciplinary team based care should be available to patients with CKD. While most provinces fund CKD care, the specific models by which these outcomes are achieved are not known. Funding for clinics is hospital or program based. OBJECTIVES: To describe the structure and function of clinics in order to understand the current models of care, inform best practice and potentially standardize models of care. DESIGN: Prospective cross sectional observational survey study. SETTING PATIENTS/PARTICIPANTS: Canadian nephrology programs in all provinces. METHODS AND MEASUREMENTS: Using an open-ended semi-structured questionnaire, we surveyed 71 of 84 multidisciplinary adult CKD clinics across Canada, by telephone and with written semi-structured questionnaires; (June 2012 to November 2013). Standardized introductory scripts were used, in both English and French. RESULTS: CKD clinic structure and models of care vary significantly across Canada. Large variation exists in staffing ratios (Nephrologist, dieticians, pharmacists and nurses to patients), and in referral criteria. Dialysis initiation decisions were usually made by MDs. The majority of clinics (57%) had a consistent model of care (the same Nephrologist and nurse per patient), while others had patients seeing a different nephrologist and nurses at each clinic visit. Targets for various modality choices varied, as did access to those modalities. No patient or provider educational tools describing the optimal time to start dialysis exist in any of the clinics. LIMITATIONS: The surveys rely on self reporting without validation from independent sources, and there was limited involvement of Quebec clinics. These are relative limitations and do not affect the main results. CONCLUSIONS: The variability in clinic structure and function offers an opportunity to explore the relationship of these elements to patient outcomes, and to determine optimal models of care. This list of contacts generated through this study, serves as a basis for establishing a CKD clinic network. This network is anticipated to facilitate the conduct of clinical trials to test novel interventions or strategies within the context of well characterized models of care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".