Reappraisal of statin primary prevention trials: implications for identification of the statin-eligible primary prevention patient
Bibliographic record
Abstract
AIMS: Identification of patients eligible for primary prevention statin therapy is complex, often relying upon risk algorithms that diverge internationally. Our goal was to develop a simpler global definition of statin-eligible primary prevention patients. METHODS AND RESULTS: Randomized clinical trials (RCTs) cited in North American and European dyslipidaemia guidelines justifying primary prevention statins for cardiovascular risk reduction were critically reappraised according to eligibility criteria and characteristics of actual enrollees. Statin-eligibility based on meeting minimal enrolment criteria vs. risks calculated using the Framingham risk score, the pooled cohort equation, and the systematic coronary risk estimate two were contrasted. Patient scenarios meeting minimal RCT eligibility criteria seldom attained high enough 10 year risk of events according to the algorithms tested and thus would not be eligible for statin therapy. Overall, enrollees were 63.9 ± 8.9 years (mean ± SD) with low density lipoprotein-cholesterol (LDL-C) 3.53 ± 0.91 mmol/L. Enrollees in trials studying the lowest LDL-C levels were generally older and had additional risk factors. CONCLUSION: Results of primary prevention RCTs justify treatment of more subjects and lower risk subjects than current risk algorithm-based guidelines. Based on a synthesis of RCT inclusion/exclusion criteria and the characteristics of enrollees, we propose that a statin-indicated primary prevention subject is one who is 40 to 70 years with a low density lipoprotein-cholesterol (LDL-C) ≥ 3.0 mmol/L or is 55 to 80 years with LDL-C ≥ 1.8 mmol/L and additional risk factors.
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 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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".