Characteristics and drug utilization patterns of new users of rosuvastatin and other statins in four countries.
Bibliographic record
Abstract
AIM: This study was undertaken to increase understanding of the utilization of a newly introduced statin through evaluation of characteristics of 'real-life' patients in a pharmacoepidemiology program in the USA, the Netherlands, the UK and Canada. METHODS: This was an observational analysis of prospectively collected data from primary care patients classified as new users of rosuvastatin or any other statin. New users (naïve or switched initiators) of rosuvastatin were compared with initiators of other statins, as identified from automated healthcare databases in the first 1 to 2 years of rosuvastatin availability. Demographics, statin doses, previous statin use and other lipid-lowering therapies, and relevant comorbidities were recorded. The main outcome measure was proportion of naïve and non-naïve statin users in patients prescribed rosuvastatin or 'other statins'. RESULTS: Among 346.547 new statin users identified in the cohorts, 46.838 (13.5%) were new users of rosuvastatin and most (84.1%) were statin-naïve. Patients receiving rosuvastatin were more likely to have been previously treated with another statin or non-statin lipid-lowering therapy and tended to be younger, compared with first users of other statins. CONCLUSION: These findings suggest that rosuvastatin is preferentially prescribed to patients who have not responded satisfactorily to established treatment.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".