Primary prevention with pravastatin for 5 years continued to prevent coronary events in the next 10 yearsCommentary
Bibliographic record
Abstract
I Ford Dr I Ford, University of Glasgow, Glasgow, UK; ian@stats.gla.ac.uk In middle-aged men with hypercholesterolaemia and no history of myocardial infarction (MI), does 5 years of treatment with pravastatin have long-term benefits for prevention of coronary heart disease (CHD)? ### Design: post-trial follow-up of a randomised, placebo-controlled trial (West of Scotland Coronary Prevention Study [WOSCOPS]). ### Allocation: unclear allocation concealment. ### Blinding: blinded during the trial period {clinicians, patients, data collectors, and outcome adjudication committees}.* ### Follow-up period: original trial follow-up was 5 years; this study followed up surviving patients (96%) for 10 more years. ### Setting: {coronary screening clinics in the UK}.* ### Patients: 6595 men {45–64 years of age}* (mean age 55 y) with no history of MI and low-density lipoprotein cholesterol concentrations ⩾155 mg/dl (4.01 mmol/l) on 2 occasions. ### Intervention: pravastatin, 40 mg …
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".