Lipid-Lowering Therapy With Statins in High-Risk Elderly Patients
Bibliographic record
Abstract
CONTEXT: The benefits of cardiovascular therapies such as statins for secondary prevention have been well documented, although they may not be optimally used in patients most likely to benefit. Ideally, aggressiveness in the use of these beneficial therapies should correlate with baseline cardiovascular risk. OBJECTIVE: To examine the association between physicians' treatment aggressiveness and baseline cardiovascular risk. DESIGN, SETTING, AND PATIENTS: Retrospective cohort study incorporating the use of multiple linked health care administrative databases covering more than 1.4 million elderly residents of Ontario. We included 396,077 patients aged 66 years or older who had a history of cardiovascular disease or diabetes while undergoing medical treatment and who were alive on April 1, 1998. Baseline cardiovascular risk was derived using a risk-adjustment index in which we modeled probability of death after 3 years of follow-up. MAIN OUTCOME MEASURE: Likelihood of statin use, stratified by baseline cardiovascular risk, after adjusting for age, sex, socioeconomic status, and rural or urban residence. RESULTS: Only 75,617 patients (19.1%) in this secondary prevention cohort were prescribed statins. In patients 66 to 74 years old, the adjusted probabilities of statin prescription were 37.7%, 26.7%, and 23.4% in the categories of low, intermediate, and high baseline risk, respectively. The likelihood of statin prescription was 6.4% lower (adjusted odds ratio, 0.94; 95% confidence interval, 0.93-0.95) for each year of increase in age and each 1% increase in predicted 3-year mortality risk. The influence of age also interacted synergistically with baseline risk on the prescription of statins (P<.001). CONCLUSIONS: We found that prescription of statins diminished progressively as baseline cardiovascular risk and future probability of death increased. Since the benefits of a therapy are dependent on the baseline risk, the maximum benefits of statins may not be fully realized until implementation of therapy includes patients at highest risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".