The need for improved uptake of the KDIGO glomerulonephritis guidelines into clinical practice in Canada: a survey of nephrologists
Bibliographic record
Abstract
BACKGROUND: The lack of glomerulonephritis (GN) guidelines has historically contributed to substantial variability in the treatment of GN. We hypothesize that there are barriers to GN guideline implementation leading to incomplete translation of the 2012 KDIGO GN guidelines into patient care, and that current practice patterns deviate from guideline recommendations. METHODS: Adult nephrologists in Canada (N = 390) were surveyed using a web-based tool. The survey of 40 questions captured physician demographics, self-reported GN case load, treatment approaches and barriers to guideline implementation. RESULTS: The response rate was 44%. Physicians report seeing six (IQR 4,10) new cases of idiopathic GN every 6 months. The majority treat ANCA GN according to guidelines, but 9-37% treat nephrotic focal segmental glomerulosclerosis or membranous nephropathy with non-recommended immunosuppression and 6-9% do not treat with any immunotherapy, whereas 26% treat subnephrotic disease with immunosuppression. The majority indicated that standardized care tools would improve patient care, but they were only available to 25-44%. Patient education tools and nursing support are unavailable to 87 and 67%, respectively; insurance coverage for immune therapies is poorly accessible to 84%, yet 86% feel this would improve care and 96% of physicians support comparing their practice with benchmarks from provincial GN registries. CONCLUSIONS: We show that 2 years after the publication of the KDIGO GN guidelines, 15-46% of Canadian nephrologists report treatment strategies not in keeping with guideline recommendations. We identify barriers to guideline implementation and widespread physician support for initiatives that address these barriers to improve patient 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".