Understanding the Current Landscape of Kidney Disease in Canada to Advance Precision Medicine Guided Personalized Care
Bibliographic record
Abstract
Purpose of Review: To understand the impact of kidney disease in Canada and the priority areas of kidney research that can benefit from patient-oriented, precision medicine research using novel technologies. Sources of Information: Information was collected through discussions between health care professionals, researchers, and patient partners. Literature was compiled using search engines (PubMed, PubMed central, Medline, and Google) and data from the Canadian Organ Replacement Register. Methods: We reviewed the impact, prevalence, economic burden, causes of kidney disease, and priority research areas in Canada. After reviewing the priority areas for kidney research, potential avenues for future research that can integrate precision medicine initiatives for patient-oriented research were outlined. Key Findings: Chronic kidney disease (CKD) remains among the top causes of morbidity and mortality in the world and exerts a large financial strain on the health care system. Despite the increasing number of people with CKD, funding for basic kidney research continues to trail behind other diseases. Current funding strategies favor existing clinical treatment and patient educational strategies. The identification of genetic factors for various forms of kidney disease in the adult and pediatric populations provides mechanistic insight into disease pathogenesis. Allocation of resources and funding toward existing high-yield personalized research initiatives have the potential to significantly affect patient-oriented research outcomes but will be difficult due to a constant decline of funding for kidney research. Limitations: This is an overview primarily focused on Canadian-specific literature rather than a comprehensive systematic review of the literature. The scope of our findings and conclusions may not be applicable to health care systems in other countries.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.027 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".