Trends in cardiovascular drug utilization and drug expenditures in Canada between 1996 and 2001.
Bibliographic record
Abstract
BACKGROUND: There is increasing interest in studying trends in drug utilization because drug costs are the fastest growing sector of the health care system. OBJECTIVES: To focus on the trends in the utilization of and expenditures for cardiovascular drugs in Canada by drug class and by province over a six-year period. METHODS: Data from the IMS Health Canada CompuScript Audit database were used for this study from the period of February 1996 to January 2002. Patterns of drug utilization and expenditures in Canada were described for cardiovascular drug classes, individual agents within classes and by provincial analyses. RESULTS: Substantial increases in both the utilization of and the expenditures for cardiovascular medications have occurred in Canada over the last six years. Newer medication classes such as angiotensin converting enzyme inhibitors and statins now comprise the majority of cardiovascular drugs prescribed, along with continued high use of diuretics. Increases in some drug classes, such as angiotensin converting enzyme inhibitors, statins and beta-blockers, appear to be based on trial evidence or guidelines. However, marketing may play a larger role in the increases in use of angiotensin receptor blockers and specific drugs, such as amlodipine besylate and atorvastatin, because their increased utilization cannot be explained by major clinical trial evidence and/or practice guidelines. CONCLUSIONS: Changes in patterns of cardiovascular drug utilization and expenditures in Canada may be associated with clinical trial evidence, clinical practice guidelines, policy changes and/or marketing initiatives.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".