Bibliographic record
Abstract
Healthcare spending in Canada has grown rapidly in recent years, especially for drugs. This paper discusses the causes of the problem and makes policy proposals. Conflicts of interest (COIs) are a frequent occurrence in medical research and lead to bias. Published studies, especially in the area of clinical trials on drugs, are much more likely to produce findings favourable to the drug when funded by the manufacturer. Bias can occur by various means, including inappropriate study design (such as giving a placebo to control subjects rather than an existing drug) and selective publication of results. COIs also frequently occur with clinical practice guidelines. High-priced (particularly new) drugs are often marketed by inappropriate means. Drug costs in Canada could be greatly reduced if doctors prescribed lower-cost alternatives where appropriate (therapeutic substitution). Proposals are made for changes in the regulatory agencies responsible for the approval of drugs, drug marketing and post-marketing surveillance. In addition, a new regulatory agency is proposed that would examine the value of drugs and medical devices in terms of clinical effectiveness and cost-effectiveness. Such an agency would set the rules for therapeutic substitution and would determine which medical interventions can be used based on agreed cost-effectiveness criteria.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".