Intensive surveillance and treatment of dyslipidemia in the postinfarct patient: evaluation of a nurse-oriented management approach.
Bibliographic record
Abstract
BACKGROUND: Lowering plasma low density lipoprotein (LDL) cholesterol concentrations in patients with established coronary artery disease is essential if recurrent cardiac events and mortality are to be prevented; however, a large proportion of patients with myocardial infarction (MI) are not screened and treated appropriately in the months immediately following hospital discharge. OBJECTIVES AND METHODS: The CHOlesterol Post-INfarct (CHOPIN) project is a nurse-centred program initiated to close the large gap between nationally centred recognized guidelines for LDL lowering and current actual practice in the secondary prevention of coronary artery disease. RESULTS: The authors report findings in 151 consecutive patients (70 years of age or less) followed for an average of 5.5+/-3.3 months. Three months after an index MI and at a time when patients started being followed in CHOPIN, 46% of patients had LDL in excess of 2.5 mmol/L and 36% had LDL greater than 3.20 mmol/L. LDL-lowering interventions undertaken comprised either consultation with a dietitian (35%) or initiation or modification of lipid-lowering medication (58%). Mean LDL on discharge from CHOPIN was 2.58+/-0.49 mmol/L, and 97% of the patients had LDL cholesterol less than 3.20 mmol/L at discharge. CONCLUSIONS: This experience shows that a nurse-based case management strategy can achieve good control of dyslipidemia in a large proportion of post-MI patients. Because intervention to lower LDL has been prospectively shown to reduce the need for coronary artery bypass and angioplasty, these results suggest that projects configured in the manner of CHOPIN should reduce hospital costs associated with cardiovascular disease.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".