Expected versus observed survival in 3 large population studies with HMG-CoA reductase inhibitors.
Bibliographic record
Abstract
OBJECTIVE: HMG-CoA reductase inhibitors (statins) can lower low-density lipoprotein (LDL). We examined how they were used in three large recent population studies, shedding new light on the relationship between cholesterol levels and survival. METHODS: Mortality observed in the placebo and treated groups of these primary and secondary prevention studies using statins was compared with the expected mortality given in existing life tables. RESULTS: In the West of Scotland Coronary Prevention Study (WOSCOPS), 6595 men with no proven coronary disease but with high baseline cholesterol were given pravastatin or placebo for 5 years. The mortality ratio (MR) was 125% when the placebo group was compared with the Canadian Insurance Association (CIA) 1986--92 ultimate mortality table. Pravastatin abolished the increased risk associated with LDL cholesterol. In the Scandinavian Simvastatin Study (4S), 4444 patients with coronary disease and high baseline cholesterol were given simvastatin or placebo for 5 years. The placebo group had a MR of 200%, compared with CIA life tables. Simvastatin decreased this increased mortality to 153%. In the Cholesterol and Recurrent Events study (CARE), 4159 patients with previous myocardial infraction and near-normal cholesterol levels were given pravastatin or placebo for 5 years. In the placebo group, the MR was 200%, compared with the CIA life tables. In patients given pravastatin, mortality was only marginally reduced to 192%. CONCLUSIONS: In primary prevention, reducing serum cholesterol abolished the increased mortality associated with high cholesterol. In secondary prevention, lipid-lowering agents improved survival in the treated group, mainly if baseline cholesterol was high.
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 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 | 0.085 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".