P624Trends in the prescribing of drugs to prevent cardiovascular disease (CVD) in England between 1998-2015
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is increasing in prevalence and is currently the leading cause of death worldwide. Primary prevention has been shown to significantly reduce the burden of CVD and therefore prescriptions of drugs used to prevent CVD are expected to increase. However, attitudes towards these medications, particularly anti-platelets and statins for primary prevention, have changed recently. Purpose: To determine the changes in the pattern of prescription of drugs used to prevent CVD. Methods: We conducted a comprehensive nationwide retrospective study. Data was obtained from the Prescription Cost Analysis system, which holds information on every prescription dispensed in the community in England, covering a population of more than 50 million people. We obtained data for all anti-platelet agents, statins, beta-blockers and ACEi/ARBs from 1998 to 2015. Results: There has been an increase in the prescription of all drugs aimed at preventing CVD over the 17 year study period except for anti-platelet agents (see Figure1). The number of prescriptions for antiplatelet agents increased linearly from 1998 to 2009 after which this number plateaued and has remained at a similar level for the subsequent 6 years.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".