Identification and Prioritization of Canadian Society of Nephrology Clinical Practice Guideline Topics
Bibliographic record
Abstract
Introduction Nephrology clinical practice guideline topics are routinely determined by clinicians and researchers, without extensive engagement of people with lived experience (PWLE) of kidney disease and their caregivers. The Canadian Society of Nephrology (CSN) Clinical Practice Guidelines Committee (CPGC) completed this modified Delphi study to incorporate diverse stakeholder perspectives in identifying and prioritizing future guideline topics. Methods We recruited nephrology clinicians, researchers, PWLE of kidney disease or their caregivers for this study. We collated literature-derived guideline topics from international and national guideline organizations that had relevance to nephrology, in addition to suggestions from participants. Consenting participants were taken through a 3 round Delphi survey process, where items were ranked on a 9-point Likert scale in terms of their importance. Based on predetermined consensus criteria, items were accepted as a priority or excluded from further consideration. We ranked the prioritized topics and compared the median ranking between clinicians or researchers and PWLE in the round where consensus was reached. Results Of the 85 consenting participants, 76 to 78 completed each Delphi round. From the initial list of 100 topics for consideration, 12 were priorities. All stakeholder groups felt it was important for PWLE to be included in topic prioritization and guideline development. The 3 most highly prioritized topics were de novo guidelines on novel therapeutics to prevent or slow progression of chronic kidney disease (CKD), recommendations for primary care, and patient-oriented guidelines on diet and exercise in kidney disease. There were no statistical differences in the median ranking between stakeholder groups ( P > 0.05). Conclusion This study will inform the future nephrology guidelines and commentaries developed by the CSN.
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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 | 0.064 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".