Biologic Drug Access and Juvenile Idiopathic Arthritis in Canada: Improving Collaboration Between Clinician Experts and Funders
Bibliographic record
Abstract
To the Editor: We read with great interest the article by LeBlanc, et al , “ Access to Biologic Therapies in Canada for Children with Juvenile Idiopathic Arthritis”1. We congratulate the authors for highlighting the challenges for Canada’s healthcare system in delivering equitable drug access across the country when healthcare is provincially delivered and there is still no national pharmaceutical program. This is particularly important in areas such as juvenile idiopathic arthritis (JIA), where because of the rarity of the underlying condition, the development of different funding decisions across provinces and territories leads to inefficient drug policy. Provincial governments should be encouraged to review the advice of national organizations such as the Canadian Agency for Drugs and Technologies in Health (CADTH) and the pan-Canadian Oncology Drug Review (pCODR). These organizations provide evidence-based information about health technologies, including drugs, with the goal of harmonizing drug funding across the country. We would like to clarify a few misconceptions in the LeBlanc article. First, the authors stated … Address correspondence to Dr. E. Cohen, Department of Pediatrics and Institute of Health Policy, Management and Evaluation, University of Toronto, The Hospital for Sick Children, 555 University Avenue, Toronto, Ontario M5G 1X8, Canada; E-mail: eyal.cohen{at}sickkids.ca
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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 | 0.010 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.029 | 0.032 |
| 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".