MétaCan
Menu
Back to cohort
Record W4385784734 · doi:10.1177/20543581231190227

Focused Jurisdictional Scan of Glomerulonephritis Medication Access in Canada: A Program Report

2023· article· en· W4385784734 on OpenAlexaffabout
Rohini Naipaul, Catherine Marques, Jenny Ng, Sean Barbour, Christine Lo, Ainslie M. Hildebrand, Valerie Siu, Bhanu Prasad, Louis‐Philippe Laurin, Lori D Wazny, Seán Armstrong, Jaclyn Tran, Maneka Sheffield, Arenn Jauhal, Michelle Hladunewich

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsNova Scotia Health AuthorityHôpital Maisonneuve-RosemontRegina General HospitalUniversity Health NetworkUniversity of Alberta HospitalUniversity of British ColumbiaAlberta Hospital EdmontonAlberta Health ServicesUniversité de MontréalUniversity of AlbertaUniversity of TorontoWinnipeg Regional Health AuthorityOntario Stroke NetworkSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineFamily medicineEculizumabPublic healthStakeholderEnvironmental healthPublic relationsNursingPolitical science

Abstract

fetched live from OpenAlex

Purpose of Program: Glomerulonephritis (GN) is a group of rare kidney diseases that is increasingly being managed with higher cost immunosuppressive (IS) agents in Canada. Ontario Health's Ontario Renal Network (ORN) oversees the management and delivery of GN services in the province. Stakeholder surveys previously conducted by ORN identified that both clinicians and patients do not perceive access to GN medications as comprehensive or timely. The program conducted a focused jurisdictional scan among 7 provinces to inform ORN initiatives to improve access to GN medications. Specifically, the program examined clinician experience with GN access, public drug coverage criteria, and timelines for public coverage for select IS agents (ie, tacrolimus, cyclosporine, mycophenolate mofetil [MMF], mycophenolate sodium, rituximab, and eculizumab) used to manage GN in adults who live in Canada. Methods: For the selected IS agents, a focused jurisdictional scan on medication access was conducted by ORN in 2018 and updated in July 2022. Information was obtained by searching the gray literature and/or credible online sources for public funding policies and eligibility criteria. Findings were supplemented by personal communications with provincial drug programs and consulting GN clinical experts from 7 provinces (ie, Alberta, British Columbia, Saskatchewan, Manitoba, Ontario, Nova Scotia, and Quebec). Key Findings: Clinicians from different provinces prescribe IS agents similarly for GN indications, despite distinctions in public drug funding policies. While patients can obtain public funding for many IS agents, for GN, most provinces rely on case-by-case review processes. In addition, provinces can vary in their funding criteria and which IS agents are listed on the public formulary. For IS agents that require prior authorization or case-by-case review, timelines vary by province with decisions taking a few days to weeks. British Columbia, with a GN-specific drug formulary, had the most integrated and efficient system for patients and prescribers. Limitations: This scan primarily relied on publicly available information for drug coverage criteria and clinician experience with access in their province. Since this scan was conducted, public drug coverage criteria and/or application processes may have changed. Implications: While patients in most provinces have similar needs and nephrologists similar prescribing patterns, gaps still exist for publicly funded GN medications. Interprovincial differences in the drugs funded, funding criteria, and application process may affect timely and equitable access to GN medications across Canada. Given the rarity of GN, a pan-Canadian funding approach may be warranted to improve the current state.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0130.024
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.020
GPT teacher head0.310
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Kidney Health and DiseaseSame topicRenal Diseases and GlomerulopathiesFrench-language works237,207