A New Multidisciplinary Model of Glomerulonephritis Care in Ontario: A Descriptive Program Report
Bibliographic record
Abstract
Purpose of Program: In 2018, Ontario Health (Ontario Renal Network) established a new multidisciplinary model of glomerulonephritis care to be available to all 27 Regional Renal Programs. This model of care was designed to fill existing gaps and ensure individuals with glomerulonephritis access to standardized, timely, and high-quality treatment close to home. This report describes the characteristics of individuals who received this care since it was established. Sources of Information: Provincial administrative health care databases. Methods: This is a descriptive study of the characteristics of adults with a registered glomerulonephritis visit provided by a multidisciplinary care team in Ontario, Canada between April 1, 2019, and March 31, 2023. Individuals were excluded if they had evidence of a kidney transplant prior to their first registered visit. Key Findings: A total of 6,926 individuals were included in the cohort. Every year since 2019, approximately 1,200 new individuals had a registered multidisciplinary visit. IgA nephropathy was the most common reported diagnosis at the first registered visit (1,407 of 6,926 [20.5%]). Over a median follow-up period of 2.7 years (interquartile range = 1.3-3.7) since their first registered visit, 420 individuals (6%) received kidney replacement therapy (maintenance dialysis or kidney transplant). Limitations: This description of individuals with registered visits underestimates the true prevalence of adults with glomerulonephritis in Ontario, as it does not capture those who did not register or who received more advanced disease management in other settings. Implications: The use of a new model of multidisciplinary glomerulonephritis care in Ontario, Canada is becoming well established. Ongoing analysis of administrative data will guide future healthcare planning and delivery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".