Impact of the <scp>JUPITER</scp> Trial on Statin Prescribing for Primary Prevention
Bibliographic record
Abstract
STUDY OBJECTIVE: As the Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial identified a new population of individuals with cholesterol levels below traditional treatment thresholds but with elevated high-sensitivity C-reactive protein (hs-CRP) levels who may benefit from primary prevention with statin therapy, we sought to evaluate the impact of this trial on the incident prescription rates of rosuvastatin alone as well as all statins in a primary prevention population. DESIGN: Population-based, cross-sectional time-series analysis. DATA SOURCE: Administrative health care databases in Ontario, Canada. PATIENTS: A total of 299,809 incident statin users 66 years or older were identified during the study period, from January 1, 2003, to March 31, 2011, who were prescribed statin therapy for primary prevention. MEASUREMENTS AND MAIN RESULTS: We evaluated the incident rate of rosuvastatin and all statin use during each quarter of the study period. Overall, no significant trends in all incident statin use were observed (p=0.99). Furthermore, no significant differences were observed in incident rates of rosuvastatin (p=0.21) or all statin (p=0.41) use after the publication of the JUPITER trial. Despite the lack of impact of the JUPITER trial on rosuvastatin or all statin utilization, the relative market share of rosuvastatin increased from 9% to 65% over the study period. CONCLUSION: The publication of the JUPITER trial did not significantly affect trends in overall statin and rosuvastatin prescribing patterns for primary prevention in this study. Increases in the relative market share of rosuvastatin may be attributed to the impact of the pharmaceutical industry on prescribing patterns. Our results highlight the need to further improve the integration of evidence-based prescribing into cost-effective clinical practice.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".