Reaching Target Lipid Levels in Patients at High Risk of Cardiovascular Event: The Experience of a Canadian Tertiary Care Lipid Clinic
Bibliographic record
Abstract
OBJECTIVES: To determine the proportion of high risk patients followed at a tertiary care lipid clinic who met recommended lipid targets and to identify predictors of reaching goal lipid levels. RESEARCH DESIGN AND METHODS: A retrospective cohort study of 502 high risk patients followed between 1983 and 2003. Clinical and demographic data and fasting lipid profiles were extracted from each patient's first two clinic visits as well as the most recent visit. RESULTS: All patients in this study were at high risk of cardiovascular events due to dyslipidemia. At "Visit 1", only 55 (11.0%) of patients were at target TC/HDL-C < 4.0, and 97 (19.3%) of patients met target LDL-C < 2.5 mmol/l. At "Visit 3", 229 (45.8%) patients reached TC/HDL-C target, and 216 (43.2%) patients were at LDL-C target. The mean change in lipid values between Visit 1 and Visit 3 was significant (p = 0.0002) for LDL-C and (p < 0.0001) for TC/HDL-C. The use of statins, niacin, or salmon oil were all significantly associated with reaching TC/HDL-C target and LDL-C target, as well male gender, diabetes mellitus and peripheral vascular disease were also associated with reaching LDL-C target. Increasing age and lower body mass index were associated with reaching goal TC/HDL-C. CONCLUSIONS: The mean absolute changes in lipid values were significant and median lipid levels approached target levels in patients followed at specialized clinic, however the majority of high risk patients are not meeting goal lipid levels.
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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 | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".