Canadian Society of Transplantation and Canadian Society of Nephrology Commentary on the 2017 KDIGO Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review an international guideline on the evaluation and care of living kidney donors and provide a commentary on the applicability of the recommendations to the Canadian donor population. SOURCES OF INFORMATION: We reviewed the 2017 Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline on the Evaluation and Care of Living Kidney Donors and compared this guideline to the Canadian 2014 Kidney Paired Donation (KPD) Protocol for Participating Donors. METHODS: A working group was formed consisting of members from the Canadian Society of Transplantation and the Canadian Society of Nephrology. Members were selected to have representation from across Canada and in various subspecialties related to living kidney donation, including nephrology, surgery, transplantation, pediatrics, and ethics. KEY FINDINGS: Many of the KDIGO Guideline recommendations align with the KPD Protocol recommendations. Canadian researchers have contributed to much of the evidence on donor evaluation and outcomes used to support the KDIGO Guideline recommendations. LIMITATIONS: Certain outcomes and risk assessment tools have yet to be validated in the Canadian donor population. IMPLICATIONS: Living kidney donors should be counseled on the risks of postdonation outcomes given recent evidence, understanding the limitations of the literature with respect to its generalizability to the Canadian donor population.
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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 | 0.023 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.018 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".