Implementation of a Kidney Genetic Service Into the Diagnostic Pathway for Patients With Chronic Kidney Disease in Canada
Bibliographic record
Abstract
Introduction: Genetic kidney disease (GKD) accounts for 10% to 20% of chronic kidney disease (CKD). Genetic testing using gene panel or targeted exome sequencing (ES) can confirm GKD; however, integration into clinical practice has been hampered by small studies, selective populations, and data predominately derived from research settings. Using prespecified clinical referral criteria and a diagnostic pipeline, we performed a prospective cohort study describing diagnostic efficacy and clinical utility of genetic assessment in patients with CKD. Methods: We analyzed a prospective cohort of 300 participants (256 families) referred to a kidney genetics clinic, between March 2020 and March 2024. Testing strategies included gene panels, and if negative or unsuitable, targeted ES analysis. Testing was performed for the detection of variants in genes known to cause CKD. Results: = 103/300) with comprehensive testing. The median time from first diagnosis of CKD to genetic assessment was long at 10.4 years. Following genetic assessment, the median time to receive a positive genetic result was 2.9 months. Multiple levels of clinical utility were recorded in patients receiving a genetic diagnosis, varying across CKD subtype. Conclusion: Instituting referral guidelines and a standardized testing algorithm established a genetic diagnosis in one-third of participants, providing insight into the viability of integrating genetic assessment in the CKD diagnostic pathway. Considering the potential for clinical utility, strategies to reduce the time from CKD diagnosis to genetics assessment are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".