Bibliographic record
Abstract
CADTH recommends that Leqvio not be reimbursed by public drug plans as an adjunct to lifestyle changes, including diet, to further reduce low-density lipoprotein cholesterol (LDL-C) levels in adults who are on a maximally tolerated dose (MTD) of a statin, with or without other LDL-C–lowering therapies, and who have nonfamilial hypercholesterolemia (nFH) with atherosclerotic cardiovascular disease (ASCVD). Evidence from 2 clinical trials showed that treatment with Leqvio lowered bad cholesterol (LDL-C) in adults with nFH with ASCVD who were already being treated with the highest possible dose of statins and in those who cannot tolerate treatment with statins. A post hoc pooled analysis of major adverse cardiovascular events (MACEs) from the ORION-10 and ORION-11 trials precluded the Canadian Drug Expert Committee (CDEC) from determining whether inclisiran reduces the risk of cardiovascular morbidity and death in adults with nFH with ASCVD. Patients identified a need for treatments that are less burdensome, can reduce bad cholesterol (LDL-C) and cardiovascular morbidity and death, and improve health-related quality of life (HRQoL); however, there was not enough evidence to show that Leqvio would reduce cardiovascular morbidity and death or improve HRQoL.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".