Bibliographic record
Abstract
Question: To determine by how much statins reduce serum concentrations of low density lipo-protein (LDL) cholesterol according to drug, dose, and duration of treatment. Population: Patients included in randomized, placebo-controlled trials of six statins (atorvastatin, fluvastatin, lovastatin, pravastatin, simvastatin, rosuvastatin). Design and methods: Meta analysis of 164 short-term, randomized trials including 24 000 drug-treated and 14000 placebo-treated patients. Studies were found by searching Medline, the Cochrane Collaboration, Web of Science databases, and BMJ.com. All double-blind, placebo-controlled trials were considered eligible. Excluded trials were those with no placebo group, any which lasted less than 2 weeks, those that used titrated doses, those that used combination drugs to lower cholesterol, crossover trials, or those with chronic renal failure patients. The efficacy of each statin was defined as the reduction of LDL for a given dose of a statin expressed as the change in the treated group minus that in the placebo group. Results: The doses of atorvastatin, lovastatin, rosuvastatin and simvastatin used to lower LDL by an absolute amount of 1.8 mmol=l or 40% are shown in the table. Pravastatin and fluvastatin were a less effective treatment, with maximum doses (80 mg=day) lowering LDL by 1.58 mmol=l and 1.60 mmol=l, respectively. Statins increased high density lipoprotein cholesterol by 0.07 mmol=l on average with no dose effects observed. For safety outcomes, 1063=14 197 statin patients compared with 923=10 568 control patients reported one or more symptoms possibly associated with the drug. Rhabdomyolysis was observed in eight statin patients compared with five placebo patients. [Table: see text] Conclusion: Statins can lower the LDL cholesterol concentration by an average of 1.8 mmol=l, and the LDL lowering effect varies across statin type and dose: simvastatin, lovastatin, atorvastatin and rosuvastatin appearing more effective, and fluvastatin and pravastatin appearing less effective.
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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 | 0.004 | 0.023 |
| 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 | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.215 | 0.109 |
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".