Secondary Stroke Prevention in Polish Adults: Results from the LIPIDOGRAM2015 Study
Bibliographic record
Abstract
Background: The purpose of the study was to evaluate secondary stroke prevention in Poland and its association with sociodemographic factors, place of residence, and concomitant cardiovascular risk factors. Material and methods: From all patients in LIPIDOGRAM2015 Study (n = 13,724), 268 subjects had a history of ischaemic stroke and were included. Results: 165 subjects (61.6%) used at least one preventive medication. Oral antiplatelet and anticoagulation agents were used by 116 (43.3%) and 70 (26.1%) patients, respectively. Only 157 (58.6%) participants used lipid-lowering drugs, and 205 (76.5%) were treated with antihypertensive drugs. Coronary heart disease (CHD) and dyslipidaemia were associated with antiplatelet treatment (p = 0.047 and p = 0.012, respectively). A history of atrial fibrillation, CHD, and previous myocardial infarction correlated with anticoagulant treatment (p = 0.001, p = 0.011, and p < 0.0001, respectively). Age, gender, time from stroke onset, place of residence, and level of education were not associated with antiplatelet or anticoagulant treatment. Only 31.7% of patients were engaged in regular physical activity, 62% used appropriate diet, and 13.6% were current smokers. Conclusions: In Poland drugs and lifestyle modification for secondary stroke prevention are not commonly adhered to. Educational programmes for physicians and patients should be developed to improve application of effective secondary prevention of stroke.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".