Low density lipoprotein cholesterol control status among Canadians at risk for cardiovascular disease: findings from the Canadian Primary Care Sentinel Surveillance Network Database
Bibliographic record
Abstract
BACKGROUND: To determine the prevalence of uncontrolled LDL-C in patients with high cardiovascular disease (CVD) risks across Canada and to examine its related factors. METHODS: Non-pregnant adults >20 years-old, who had a lipid test completed between January 1, 2009 and December 31, 2011 and were included in the Canadian Primary Care Surveillance Network (CPCSSN) database were studied. The Framingham-Risk-Score was calculated to determine the risk levels. A serum LDL-C level of >2.0 mmol/L was considered as being poorly controlled. Patients with a previous record of a cerebrovascular accident, peripheral artery disease, or an ischemic heart disease were regarded as those under secondary prevention. Logistic regression modeling was performed to examine the factors associated with the LDL-C control. RESULTS: A total of 6,405 high-risk patients were included in the study and, of this population, 68% had a suboptimal LDL-C, which was significantly associated with the female gender (OR: 3.26; 95% CI: 2.63-4.05, p < 0.0001) and no medication therapy (OR: 6.31, 95% CI: 5.21-7.65, p < 0.0001). Those with comorbidities of diabetes, hypertension, obesity, and smokers had a better LDL-C control. Rural residents (OR: 0.64, 95% CI: 0.52-0.78, p < 0.0001), and those under secondary prevention (OR: 0.42; 95 % CI: 0.35-0.51, p < 0.0001), were also more likely to have a better LDL-C control. CONCLUSION: A high proportion of high-cardiac risk patients in Canadian primary care settings have suboptimal LDL-C control. A lack of medication therapy appears to be the major contributing factor to this situation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".