PUBLIC REIMBURSEMENT OF PRESCRIPTION DRUG USED FOR OFF-LABEL INDICATIONS IN ONTARIO
Bibliographic record
Abstract
BACKGROUND: A Canadian Agency for Drugs and Technologies in Health (CADTH) therapeutic review concluded that bevacizumab and ranibizumab have similar efficacy and safety in treating retinal conditions and recommended bevacizumab be used as preferred initial therapy based on a cost-saving perspective. Such use would be off-label because bevacizumab is not approved for these conditions and has a serious safety warning (SSW) in its Product Monograph (PM) about intravitreal use. OBJECTIVE: To evaluate whether the Ontario Public Drug Programs (OPDP) reimbursement is provided only for off-label use for serious, life-threatening or severely debilitating conditions and not when the drug's PM contains a SSW against such use. METHODS: OPDP reimbursement criteria for non-palliative drugs from its frequently-requested Exceptional Access Program (EAP) and Limited Use (LU) lists were compared with approved indications and SSWs in the drugs' PMs. RESULTS: Of 125 unique frequently-requested non-palliative EAP drugs, 12 included off-label use for serious conditions for which no alternative treatment exists. Eight of the 12 had SSWs, but only one warning for orally-administered sirolimus related directly to the OPDP-reimbursed off-label use. Of 29 injectable non-palliative LU drugs, one had off-label LU criteria allowing reimbursement for an unapproved indication and a SSW unrelated to the reimbursable indication. CONCLUSION: Presently, OPDP only reimburse drugs for off-label use for the treatment of serious, life-threatening or severely debilitating conditions for which no alternative treatment exists. OPDP should not diverge from this approach by allowing cost-savings to trump appropriate drug use, which would set a unique and unprecedented example.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".