Impact of guidelines on health care use for the management of dyslipidemia in two Canadian provinces, Alberta and Nova Scotia, from 1990 to 2001.
Bibliographic record
Abstract
BACKGROUND: Guidelines for the treatment of hyperlipidemia aim at improving the management of people at a higher risk of developing cardiovascular disease. OBJECTIVES: To study the potential impact of hyperlipidemia guidelines on health care use in two Canadian provinces with different levels of hyperlipidemia. METHODS: Trends in physician billing were obtained from Alberta between 1990 and 2000 and from Nova Scotia between 1994 to 2001 using the 272 primary diagnostic code for hyperlipidemia. Record linkage between a 272 code and a prescription in the subsequent six months was made through the Pharmacare database (which automatically registers all individuals 65 years of age and over). Data were also linked between the 1995 Nova Scotia Health Survey and the Pharmacare data. RESULTS: Trends in hyperlipidemia codes were similar in Alberta and Nova Scotia by sex and age, with acceleration in the final years of the study. Approximately 5% of the adult population had a diagnosis of hyperlipidemia. Less than 60% of people aged 65 years and over with a 272 code filled an antilipemic prescription in the subsequent six months. Using the National Cholesterol Education Program Adult Treatment Panel III classification and the 1995 Nova Scotia Health Survey, less than 10% of the participants aged 65 years and over had a corresponding diagnostic code of 272, while more than half could be classified as having hyperlipidemia. In 1995, approximately one-half of people at high risk, with a 272 code in the subsequent five years, had a prescription for antilipemic drugs. CONCLUSIONS: Despite some limitations, these data show a discrepancy between guideline development and practice, leaving a high number of at-risk individuals undiagnosed and untreated. Mechanisms need to be put in place to ensure better classification and follow-up of people with hyperlipidemia at risk for cardiovascular disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".