Consistent LDL‐C response with evolocumab among patient subgroups in PROFICIO: A pooled analysis of 3146 patients from phase 3 studies
Bibliographic record
Abstract
BACKGROUND: Evolocumab significantly lowers low-density lipoprotein cholesterol (LDL-C) when dosed 140 mg every 2 weeks (Q2W) or 420 mg monthly (QM) subcutaneously. HYPOTHESIS: LDL-C changes are comparable among different patient subgroups in a pooled analysis of data from phase 3 trials. METHODS: A total of 3146 patients received ≥1 dose of evolocumab or control in four 12-week phase 3 studies. Percent change from baseline in LDL-C for evolocumab 140 mg Q2W or 420 mg QM vs control was reported as the average of week 10 and 12 values. Quantitative and qualitative interactions between treatment group and subgroup by dose regimen were tested. RESULTS: In the pooled analysis, treatment differences vs placebo or ezetimibe were similar for both 140 mg Q2W and 420 mg QM doses across ages (<65 years, ≥65 years); gender; race (Asian, black, white, other); ethnicity (Hispanic, non-Hispanic); region (Europe, North America, Asia Pacific); glucose tolerance status (type 2 diabetes mellitus, metabolic syndrome, neither); National Cholesterol Education Program risk categories (high, moderately high, moderate, low); and European Society of Cardiology/European Atherosclerosis Society risk categories (very high, high, moderate, or low). Certain low-magnitude variations in LDL-C lowering among subgroups led to significant quantitative interaction P values that, when tested by qualitative interaction, were not significant. The incidences of adverse events were similar across groups treated with each evolocumab dosing regimen or control. CONCLUSIONS: Consistent reductions in LDL-C were observed in the evolocumab group regardless of demographic and disease characteristics.
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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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.032 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".