Statin-prescribing trends for primary and secondary prevention of cardiovascular disease.
Bibliographic record
Abstract
OBJECTIVE: To determine the proportion of patients receiving statins for primary or secondary prevention of cardiovascular disease (CVD), as well as to describe lipid-screening trends. DESIGN: Retrospective chart review using the Manitoba Primary Care Research Network repository. SETTING: Manitoba. PARTICIPANTS: A total of 149 262 patients. MAIN OUTCOME MEASURES: Proportion of patients who were taking statins for primary or secondary prevention of cardiovascular disease (CVD), who did not have evidence of CVD recorded in their charts, and who underwent lipid screening; distribution of statins among age groups; and the proportion of patients eligible for lipid screening when the age cutoffs of the 2012 and 2016 Canadian Cardiovascular Society guidelines were applied. RESULTS: Of the 149 262 patients, 139 025 (93%) did not have CVD recorded in their electronic medical records and made up the primary prevention group; of these 139 025 patients, 5955 (4%) were taking statins. Also in the primary prevention group, 14 814 (11%) patients were 75 years of age and older; of these patients, 1374 (9%) were taking statins. A total of 10 237 of the 149 262 (7%) patients had CVD recorded in their charts (secondary prevention group); 3013 (29%) of these patients were taking statins. When the 2016 Canadian Cardiovascular Society guidelines age cutoffs were applied, 56% of patients (83 119 of 149 262) were eligible for lipid screening, and 31% (26 024 of 83 119) of them had evidence of screening in the past 5 years. Of the total population of those aged 75 and older, 28% (5597 of 20 188) had undergone lipid screening. Of the total population taking statins, 28% (2481 of 8968) had undergone lipid testing while taking statins. CONCLUSION: In Manitoba, less than 5% of the primary prevention population and less than 30% of the secondary prevention population had received repeat statin prescriptions from their primary care providers. This represents a possible practice gap that warrants future research, as statins offer considerable morbidity and mortality benefits in these patients.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".