Does the Prevalence of Dyslipidemias Differ between Newfoundland and the Rest of Canada? Findings from the Electronic Medical Records of the Canadian Primary Care Sentinel Surveillance Network
Bibliographic record
Abstract
INTRODUCTION: Newfoundland and Labrador (NL) has the highest prevalence of cardiovascular disease (CVD) in Canada. Dyslipidemia is a risk factor for CVD. This study compares the prevalence of dyslipidemia in the NL population with the rest of Canada. METHODS: A cross-sectional study, using data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), was undertaken. The study population included adults, excluding pregnant women, aged 20 years and older. Canadian guidelines were used for classifying dyslipidemia. Univariate and multivariate analyses were conducted to compare the lipid levels and prevalence of dyslipidemia between NL and the rest of Canada. RESULTS: About 128,825 individuals (NL: 7,772; rest of Canada: 121,053) were identified with a mean age of 59 years (55% females). Mean levels of total cholesterol (4.96 vs. 4.93, p = 0.03), low-density lipoprotein (LDL) (3.00 vs. 2.90 mmol/L, p < 0.0001), triglyceride (1.47 vs. 1.41 mmol/L, p < 0.0001), and high-density lipoprotein (HDL) (1.29 vs. 1.39 mmol/L, p < 0.0001) were significantly different in NL compared to the rest of Canada. Dyslipidemias of LDL (29 vs. 25% p < 0.0001), HDL (38 vs. 27%, p < 0.0001), and triglyceride (29 vs. 26%, p < 0.0001) were significantly more common in NL. After adjustment for confounding variables, NL inhabitants were more likely to have dyslipidemia of total cholesterol (OR: 1.16, 95% CI: 1.10-1.23, p < 0.0001), HDL (OR: 1.52, 95% CI: 1.44-1.60, p < 0.0001), LDL (OR: 1.38, 95% CI: 1.30-1.46, p < 0.0001), and ratio (OR: 1.53, 95% CI: 1.42-1.60, p < 0.0001). CONCLUSION: The NL population has a significantly higher rate of dyslipidemia compared to the rest of Canada, and the mean levels of all lipid components are worse in NL. Distinct cultural and genetic features of the NL population may explain this, accounting for a higher rate of CVD in NL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".