Primary Care Provider Adherence to the Canadian Diabetes Association Clinical Practice Guideline for Chronic Kidney Disease
Bibliographic record
Abstract
Background: Diabetes mellitus is the leading cause of chronic kidney disease (CKD) requiring dialysis and contributes to one-half of all new dialysis cases each year in Canada. Despite the ability to stop or slow the progression of CKD through early detection and intervention, CKD continues to rise, in part, due to providers’ lack of knowledge of and adherence to established national clinical practice guidelines (CPGs). Methods: A quality improvement project was implemented in a rural, primary care clinic to enhance provider knowledge of the current CPG recommendations for CKD screening before and after a provider-specific educational intervention. Results: The educational intervention improved provider knowledge of and confidence in screening for renal disease in diabetic patients. The average numbers of diabetic patients screened for renal disease improved each year, with 85.5% being screened in 2015-2016, resulting in a net increase of 31.5%. In addition, modifiable risk factor screening by providers also improved in the same period, including measures of weight, blood pressure, lipids, and glycosylated hemoglobin levels. Conclusion: Increasing primary provider awareness and knowledge, through education, can foster early recognition and management of CKD in diabetes and ultimately improve renal health outcomes in the diabetic population.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".