Efficacy and safety of inclisiran based on background lipid-lowering treatment
Bibliographic record
Abstract
AIM: To evaluate whether the effect of inclisiran to lower LDL-C varied by background lipid-lowering therapy (LLT). METHODS: In ORION-10 and ORION-11 Phase 3 trials, patients (N=3178) with atherosclerotic cardiovascular disease (ASCVD) or ASCVD risk equivalents were randomized 1:1 to receive inclisiran or placebo on Day 1, Day 90, and 6-monthly thereafter. In this pooled post hoc analysis, patients were stratified by baseline combination therapy (statin plus ezetimibe [n=214; 6.7%]), monotherapy (statin [n=2711; 85.3%] or ezetimibe [n=54; 1.7%]), or neither LLT (none [n=199; 6,3%]) into therapy groups with/without other LLT. Stratification by baseline statin intensity was also performed (N=3155). Relative and absolute changes in LDL-C were assessed. RESULTS: Mean (±SD) baseline LDL-C was 2.7 mmol/L (±1.0), 2.6 mmol/L (±0.9), 3.7 mmol/L (±1.7), and 4.1 mmol/L (±1.5) among patients receiving combination, statin, ezetimibe, or neither LLT, respectively. In these categories, mean (95% confidence interval [CI]) time-adjusted, placebo-corrected percentage change in LDL-C after Day 90 to Day 540 with inclisiran were -57.0% (-63.8, -50.1), -51.5.% (-53.4, -49.7), -50.5.% (-59.6, -41.5), and -43.1% (-48.7, -37.6); corresponding absolute changes were -1.5 mmol/L (-1.7, -1.3), -1.3 mmol/L (-1.4, -1.3), -1.7 mmol/L (-2.0, -1.4), and -1.7 mmol/L (-1.9, -1.5). CONCLUSION: Sustained and effective LDL-C lowering with inclisiran was observed irrespective of background LLT treatment. Inclisiran was overall well tolerated with all background LLT treatments, consistent with its established safety profile.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".