Reducing costs and improving hypertension management.
Bibliographic record
Abstract
OBJECTIVE: To quantify the cost-savings that could be realized by switching patients from two separate agents, ACE inhibitor/ARB and thiazide diuretic, to a fixed dose combination product. METHODS: CompuScript and Longitudinal Rx (LRx) Insights data from IMS Health Canada for Oct 2006-Sept 2007 was used. From the LRx data, the proportion of patients taking both ACE inhibitors/ARBs and thiazide diuretics as two separate products was calculated to determine how many would qualify for a combination product. From the CompuScript data, the total number of prescriptions for ACE inhibitors and ARBs and the actual average dollar value per prescription for thiazide diuretics, ACE inhibitors, ARBs, and ACE inhibitor/ARB with thiazide diuretic combination products was used to determine the potential cost savings of switching from two separate drugs to a combination product. As a sensitivity analysis, the proportion of patients receiving two separate products who could be switched to a combination product was varied from 60-100%. This analysis was done for Alberta and Canada. RESULTS: The conversion of ACE inhibitor/ARB and thiazide diuretic as two separate agents to a combination product could potentially result in a yearly cost-savings of $27 to $45 million for Canada ($1.1 to $1.9 million for Alberta), based on 60-100% conversion to a combination product. CONCLUSIONS: The present analysis has shown that a simple intervention of converting patients receiving separate ACE inhibitor/ARB and thiazide diuretic prescriptions to a single combination product prescription will produce substantial cost-savings for the health care system and simplify the medication regimen for patients.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".