Control of baseline cardiovascular risk factors in the SU-FOL-OM3 study cohort: does the localization of the arterial event matter?
Bibliographic record
Abstract
AIM AND METHOD: No data are currently available on the prevalence and control of cardiovascular (CV) risk factors in secondary prevention depending on the cardiac or cerebral localization of the ischemic disease. We investigated the prevalence and control of modifiable CV risk factors, as well as the determinants of CV risk factors' control and adequate treatment in a secondary prevention cohort, the SU-FOL-OM3 study cohort, to determine the role of the localization of the ischemic disease including events. RESULTS: A total of 2491 patients were included in the study. The prevalence of all modifiable risk factors was high in both coronary heart disease and cerebrovascular disease (CVD) groups. Control of all risk factors and the presence of antiplatelet medication were noted in 29.6% of patients with coronary heart disease and 11% of patients with CVD. The cardiac localization of the including event was independently associated with the control of each of the risk factors studied (hypertension, low-density lipoprotein-cholesterol, smoking) and to the control of all risk factors present and prescription of antiplatelet therapy with an odds ratio (95% confidence interval) of 2.72 (1.97-3.75). CONCLUSION: There is a need to improve the control of CV risk factors in secondary prevention patients. This is particularly crucial for patients with CVD.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".