Evaluation of Indications for Genetic Assessment in Living Kidney Transplant Donors and Relevant Canadian Practices in Light of the Current International Guidelines
Bibliographic record
Abstract
Background: End stage renal disease (ESRD) is a prevalent condition with tendency for familial clustering. Living kidney donor transplantation is a superior treatment option, however, up to 40% of living kidney transplant donors (LKTDs) are biologically related to their recipients which subjects recipients to worse graft survival and donors to higher future risk of ESRD. Genetic testing of potential LKTDs could improve risk assessment and inform safety of donation, however, the strategies to evaluate these donors are still evolving. In this context, the standard use of genetic testing for LKTDs in Canada is unknown. Methods: International guidelines were reviewed to compare the indications for genetic assessment of LKTDs. Surveys were sent to 25 Canadian adult transplant centers to examine their protocols and relevant practices for LKTDs genetic assessment. Results: Response rate was 70%. Generally, donor's family history of chronic kidney disease does not preclude donation after informed discussion regarding risks and benefits. Autosomal Dominant Polycystic Kidney Disease, Alport syndrome and atypical Hemolytic Uremic Syndrome are the most frequently encountered conditions. Based on our case scenario questions, most centers assess LKTDs on a case-by-case basis and a minoroity have specific policies for donor genetic evaluation. The current Canadian transplant centers practices generally align with available international guidelines' recommendations. The most cited guidelines are KDIGO, CSN/CST, and Kidney Paired Donation Protocol. Conclusions: Canadian transplant centers have diverse strategies for genetic evaluation of LKTDs, mostly based on case-by-case assessment. Current recommendations are largely based on expert opinion due to lack of a reliable body of evidence and inefficiency of the current testing modalities. More studies are needed to provide stronger, evidence-based recommendations to ensure safety of donation. Prognostic risk assessment scores could be helpful for better quantification of long term effects of abnormal genetic testing.Fig 1:: Percentage of the most commonly encountered genetic conditions in the Canadian transplant centers [Blue] versus percentage of center having donor evaluation protocols for the same conditions [Red].
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".