Getting the Cat Back in the Bag: Reforming the Way Provinces Manage Drug Expenditures to Make Them Manageable
Bibliographic record
Abstract
The fiscal "cat" of healthcare spending - drug expenditures - is out of the bag: drug costs are now the fastest rising component of healthcare expenditures in Canada. Laupacis, Anderson and O'Brien describe the current process of listing drugs on the provincial drug formulary in Ontario, identify factors that may contribute to the rapid growth in drug expenditures, and make a number of recommendations for controlling drug expenditures, including (1) improving the evidence on cost-effectiveness; (2) disseminating the evidence to prescribers; (3) re-evaluating the evidence; and (4) increasing the transparency about the acquisition costs of drugs. These are recommendations that, if implemented, would theoretically help decision-makers make more rational decisions about which drugs to list on provincial formularies. The question of how to implement the recommendations remains to be elucidated, as does an evaluation of the trade-offs between costs and benefits of obtaining better information on cost-effectiveness.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".