Assessment of stroke awareness in two Transylvanian cities
Bibliographic record
Abstract
Abstract Acute cerebrovascular disease (stroke) occupies a prominent place among the leading causes of death and morbidity. The effectiveness of its prevention and emergency treatment is closely linked to the health literacy of the general population. In Hungarian-speaking regions, limited data are available regarding public awareness of stroke, and Romanian surveys have only tangentially addressed the issue. In our study, we analyzed data from a questionnaire-based survey conducted in Sfântu Gheorghe (Sepsiszentgyörgy) and Reghin (Szászrégen) between 2019 and 2020. Based on 851 evaluable responses, the level of awareness was found to be low regarding both symptoms and modern treatment procedures. The most frequently recalled symptom was dizziness; specific symptoms corresponding to the “FAST” principle (facial asymmetry, hemiparesis, or speech impairment) were spontaneously mentioned by fewer than one quarter of the respondents. Regarding risk factors, respondents identified psychosocial stress at a higher rate than more substantial risks such as diabetes mellitus or physical inactivity. Among modern treatment procedures (thrombolysis or thrombectomy), at least one was spontaneously mentioned by only 17 respondents, although the recognition rate increased to nearly 50% in a multiple-choice format. Based on logistic and linear regression models, the primary predictor of awareness was educational attainment: the knowledge of those with lower education levels lagged behind that of university graduates in nearly all analyzed aspects. Furthermore, male gender and obesity were negatively associated with the level of awareness, and regional differences were also identified. Our results may assist in more accurately defining the target groups for future public health educational campaigns.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".