Impact of a policy allowing for over-the-counter statins.
Bibliographic record
Abstract
BACKGROUND: allowing statins to be sold without a prescription has been widely debated in Great Britain, Canada and the USA. AIMS: To examine the impact of the recent policy in Great Britain allowing patients to purchase over-the-counter (OTC) 10 mg dose of a cholesterol-lowering drug (simvastatin) on meeting the needs of individuals at moderate risk of coronary heart disease (CHD). METHODS: we undertook an analysis of adults (aged >20 years) in the nationally representative 2006 Health Survey for England (HSE, n =10 007). We evaluated the proportion of individuals using OTC statins among all adults aged 20 years and over and among individuals eligible for them according to current guidelines. RESULTS: among adults in the HSE, 44.1% met the criteria for referral to a general practitioner for CHD risk, 9.7% were eligible for OTC statins and 46.1% did not meet the eligibility for prescribed or OTC statin therapy. Less than 1% (0.7%) were taking OTC statins. The proportion of individuals using OTC statins among those eligible for them was low and comparable to that in the overall population (0.2%). Among those taking OTC statins, 71.5% were also taking prescribed lipid lowering agents. CONCLUSION: these results suggest that the aim of addressing the treatment gap among individuals at moderate risk by offering access to OTC statins has not been achieved two years after the implementation of the policy.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".