Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent Canadian lipid guidelines recommend that all high-risk patients receive medication to reduce low density lipoprotein cholesterol (LDL-C) below 2.5 mmol/L. The recently published Reversal of Atherosclerosis with Aggressive Lipid Lowering (REVERSAL) and Pravastatin or Atorvastatin Evaluation and Infection Therapy (PROVE IT) studies compared strategies of cholesterol lowering with atorvastatin 80 mg versus pravastatin 40 mg. Atorvastatin halted the progression of atherosclerosis (whereas atherosclerosis progressed in the patients receiving pravastatin), and resulted in a 16% reduction in the primary composite end point (all-cause death, myocardial infarction, unstable angina, revascularization and stroke) compared with the pravastatin-treated group. In the PROVE IT trial, LDL-C was reduced by atorvastatin to 1.6 mmol/L and by pravastatin to 2.46 mmol/L. Although lower LDL-C levels are one explanation for the improved outcomes with atorvastatin, pleiotropic differences of the two statins, such as their effects on inflammation and coagulation, cannot be excluded. Until trials are completed that compare outcomes from LDL-C lowering to different targets with the same statin, it is premature to recommend changes to the current Canadian guidelines. However, future recommendations may suggest much lower LDL-C targets than those currently recommended.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it