Guidelines Oriented Approach to Lipid (GOAL) Lowering Quality Improvement International Program
Bibliographic record
Abstract
Background: Despite practice guidelines, strategies for lowering LDL-C are often poorly adopted in clinical practice. Materials and Methods: Five countries (Brazil, Kuwait, Mexico, Saudi Arabia, and UAE) enrolled 2,422 patients with atherosclerotic cardiovascular disease (ASCVD) or familial hypercholesterolemia (FH) with low density lipoprotein cholesterol level (LDL-C) above 1.4 mmol/L. Patients were followed at 6 ± 2 months intervals to assess LDL-C level and treatment with ezetimibe and/or proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9i). Results: 2422 patients 60.4 ± 11.7 years old and 29% women were enrolled from 87 participating cardiology sites. Overall, 91.1% of patients had coronary artery disease and FH in 12.3%. At baseline LDL-C was 2.96 ± 1.36 mmol/L and 1.87 ± 1.28 mmol/L (p<0.0001) at last available observation (n=2014). Proportion of patients achieving LDL<1.4 mmol/L (primary endpoint) increased from zero to 41.4% (p<0.0001). At baseline, 99.2% of patients were on statin (81.3% high intensity statin), 34.4% on ezetimibe and its use increased significantly (62.9%, p<0.0001). PCSK9i use increased to 35.0% from baseline to last follow up (p<0.0001). Clinical outcomes such as ACS, CVA/TIA, PCI, CABG, or hospitalization for ASCVD reasons were recorded in 10.7% of patients during the follow up. Patients with no event had an overall LDL-C of 1.90 ± 1.3 mmol/L while those with an event had LDL-C significantly higher at the visit immediately prior to event (2.70 ± 1.35, p=0.0001). Conclusion: The results indicate the feasibility of overcoming treatment inertia and improving LDL-C control which should help to achieve reduction in cardiovascular morbidity and mortality in ASCVD patients.
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 | 0.047 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".