Bibliographic record
Abstract
Although statins improve survival and re duce the risk of cardiovascular eventsin populations at high and moderate risk,1 their effectiveness and cost-effectiveness in low-risk populations is less certain.2 This uncertainly is due in part to low-risk patients being less likely to have cardiovascular events over the short term. For instance, in the recent Justifi cation for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) study3 — a large randomized trial comparing cardiovascular outcomes in low-risk patients randomly assigned to receive either rosuvastatin or placebo — the risk of death or nonfatal myocardial infarction over three years was 2.5 % in the rosuvastatin group and 3.5 % in the placebo group, which rep-resented a large relative, but small absolute, risk reduction in cardiovascular events. Other cholesterol-lowering interventions are available, such as diet, exercise and the use of other hypolipidemic agents, but the use of statins is the only such intervention known to reduce cardiovascular risk in people with low and high blood cholesterol levels.4–7 Thus, statins are now primarily indicated for the reduction of cardio-vascular risk instead of being used mainly for the management of hypercholesterolemia.8 With this broadening indication for use, ex-penditures on statins have increased and rep resent about 13 % of total expenditures by pro vincial formularies in Canada.9 The absolute number of people at low cardiovascular risk who are taking statins has increased substantially over the last decade, driven by the large number of low-risk people in the general population.10 In addition, statins that are more effective in lowering low-Cost-effectiveness of the use of low- and high-potency statins in people at low cardiovascular risk
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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.498 | 0.173 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".