Are patients with hyperlipidemia undertreated? Study of patients admitted to hospital with coronary events.
Bibliographic record
Abstract
OBJECTIVE: To identify patients admitted to hospital with coronary events and to estimate their pre-admission coronary risk, including their lipid levels. Despite the available data and numerous guidelines, evidence indicates that many patients with hyperlipidemia are undertreated and are not achieving target lipid levels. DESIGN: Retrospective chart review. SETTING: Acute care community hospital in Winnipeg, Man. PARTICIPANTS: A total of 153 patients who were diagnosed with acute myocardial infarction, unstable angina, or acute coronary syndrome upon admission. METHOD: Each patient's 10-year risk of developing coronary artery disease was calculated, and his or her risk status was established. Each patient's low-density lipoprotein cholesterol (LDL-C) levels were recorded and categorized based on current Canadian guidelines. RESULTS: Mean age of patients was 67.6 years; 60.8% were male. Patients in the low-risk category had a mean LDL-C level of 2.98 mmol/L (95% confidence interval [CI] 2.66 to 3.29), and patients in the moderate-risk category had a mean LDL-C level of 3.01 mmol/L (95% CI 2.74 to 3.28), both significantly lower (P < .05) than the LDL-C target levels for patients in those risk categories according to Canadian guidelines. The mean LDL-C level for patients in the very high-risk category, however, was 2.53 mmol/L (95% CI 2.35 to 2.71), above the recommended goal. Almost half the patients (48.3%) in the very high-risk category had LDL-C levels that exceeded the goal. Slightly more than 1 in 3 patients in the very high-risk category was reported to be taking lipid-lowering agents. CONCLUSION: Patients in the community who are at very high risk of having cardiovascular events are undertreated with respect to attaining LDL-C target levels. These findings point to an opportunity to prevent patient morbidity and reduce the number of hospitalizations for cardiovascular events.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".