The BC Glomerulonephritis Network: Improving Access and Reducing the Cost of Immunosuppressive Treatments for Glomerular Diseases
Bibliographic record
Abstract
Glomerulonephritis (GN) is a common cause of end-stage renal disease in Canada and worldwide, and results in significant health care resource utilization and patient morbidity. However, GN has not been a traditional priority of provincial renal health care organizations, despite the known benefits to health services delivery and patient outcomes from integrated provincial care in other types of chronic kidney disease. To address this deficiency, the British Columbia (BC) Provincial Renal Agency created the BC GN Network in 2013 to coordinate provincial GN health services delivery informed by robust population-level data capture on all GN patients in the province via the BC GN Registry. This report describes the use of the BC GN Network infrastructure to systematically develop and evaluate a provincial GN drug formulary to improve patient and physician access to evidence-based immunosuppressive treatments for GN in a cost-efficient manner that successfully halted historical trends of increasing medication costs. An example is provided of using the provincial infrastructure to implement and subsequently evaluate an evidence-informed health policy of converting brand to generic tacrolimus for the treatment of GN. The BC GN Network, including the provincial drug formulary and data infrastructure, is an example of the benefits of expanding the mandate of provincial renal health administrative organizations to include the care of patients with GN, and constitutes a viable health delivery model that can be implemented in other Canadian provinces to achieve similar goals.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".