Key questions resulting from the JUPITER trial assessing cardiovascular disease intervention with rosuvastatin
Bibliographic record
Abstract
THIS PAPER PRESENTS AN ANALYSIS OF THE RECENTLY PUBLISHED JUSTIFICATION FOR THE USE OF STATINS IN PREVENTION (JUPITER: an intervention trial evaluating rosuvastatin) trial, which tested the statin rosuvastatin in apparently healthy individuals with no prior cardiovascular (CVD) disease and with normal plasma low density lipoprotein (LDL) cholesterol concentrations but with raised plasma high sensitivity C-reactive protein (hsCRP) levels. The rate of the combined primary CVD endpoint was significantly reduced in the treatment arm after a median of under 2 years. The JUPITER trial is distinct from previous studies examining statin use in primary prevention groups because the target group for drug therapy was apparently healthy men and women at low or intermediate risk for developing CVD. On the basis of JUPITER's findings, there are key questions that should be assessed on the therapeutic intervention of CVD regarding: the primary prevention groups that should be targeted for statin therapy, the utility of targets in addition to plasma LDL cholesterol levels, and the need to consider the metabolic state of individuals targeted for therapy (including the presence of obesity and inflammation). The conclusion from the current analysis is that the JUPITER results warrant further LDL cholesterol lowering than is currently targeted in primary prevention groups that have a pre-existing condition or lifestyle that elevates CVD risk but still do not have a high global CVD risk (as assessed with current algorithms). This group is not captured in current widely used CVD risk calculations, however, with the identification of useful biomarkers, such as hsCRP, this group can be better identified and targeted for intervention.
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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 | 0.225 | 0.391 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".