Nonconventional diabetes-related care strategies for patients with chronic kidney disease: A scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Patients with diabetes and chronic kidney disease (CKD) are at high risk of diabetes-related complications. Diabetes care can support these individuals, but outpatient clinic appointments can be difficult to attend, given their already high burden of multimorbidity. METHODS: We systematically searched the medical and grey literature for studies that evaluated the effect of nonconventional diabetes care strategies on diabetes-related outcomes in adults with stages 2-5 CKD or using dialysis (end of search December 30, 2017). We included both randomized-controlled trials and observational studies. Study selection and data extraction were completed by two independent reviewers. Diabetes-related outcomes included glycemic, blood pressure, and lipid control, along with microvascular complications, macrovascular complications, and death. RESULTS: After screening 2177 relevant citations, we identified 34 studies which met inclusion. The majority were observational studies. Studies were frequently small, single-centered, and excluded patients with more advanced CKD. Nonconventional diabetes care strategies included community-based care, unique self-management and education programs, nurse-led care clinics, dialysis-based diabetes programs, telemedicine, and interdisciplinary care clinics. Programs were most often developed by study investigators. Although there were limitations to several of the included studies, programs were described to have modest effects on physiologic outcomes, and in some cases, diabetes-related complications and death. CONCLUSIONS: Nonconventional diabetes-related care might be helpful to patients with CKD. Prior to developing and implementing programs, however, it will be important to study them more rigorously, understand their acceptability to patients, and evaluate their costs and feasibility in a real-world setting.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".