Exploring patient demographic and clinical characteristics associated with lipid-lowering pharmacotherapy use in primary care
Bibliographic record
Abstract
BACKGROUND: Lipid-lowering therapeutics, particularly HMG Co-A reductase inhibitors, can be beneficial in primary and secondary cardiovascular prevention. The Canadian population frequently uses these medications but the manner in which they are used in community-based practice is unknown. OBJECTIVES: To assess the patient characteristics associated with lipid lowering drug use in community-based clinical practice across four geographic regions in Canada. To assess amongst lipid-lowering drugs users the proportion of patients that would meet accepted dyslipidemia management guidelines. To assess the community-based effectiveness of anti-hyperlipidemic drugs. METHODS: Patients filling a prescription for any anti-hyperlipidemia therapy in selected pharmacies in Ontario (ON), Quebec (PQ), British Columbia (BC) and Nova Scotia (NS). All eligible patients were interviewed over the telephone. Physicians who were providing healthcare to the participating patients were requested to provide information from the patient's medical record. RESULTS: The mean patient age was > 60 yr in all four provinces. There were some differences amongst the four provinces pertaining to patient characteristics, prescription patterns and therapeutic indicators, but not to outcomes. Anti-hyperlipidemia therapy was associated with a 1.81 mmol/L decrease in LDL-Cholesterol (P < 0.001); however only 73% of patients achieved target LDL-Cholesterol concentrations. A lag time of 1.96 yr (P < 0.0001) was observed from the diagnosis of dyslipidemia until the drug treatment was initiated. Patients had an average of 2.8 cardiovascular (CV) risk factors and 86% of patients had at two or more CV risk factors. Thirty-nine percent (95% CI, 36% - 42%) of the patients were being treated for secondary prevention. Thirteen percent (11-16%) of patients who were being treated for primary prevention had diabetes. Metabolic syndrome was observed in 32% (29-35%) of patients. CONCLUSION: Almost all patients fulfilled guideline requirements for the use of anti-hyperlipidemic therapy. Although the use of pharmacotherapy was associated with a lowering of LDL cholesterol more aggressive management is required to attain target LDL cholesterol concentrations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".