Assessment of Knowledge on Risk Factors, Warning Signs, and Early Treatment Approaches of Stroke among Community Adults in North India: A Telephone Interview Survey
Bibliographic record
Abstract
Abstract Background As stroke is a catastrophic illness, knowing its risk factors, early signs and symptoms, and management strategies could potentially prevent morbidity and mortality among the people. Objectives The aim of this study is to determine the public cognizance of risk factors, signs and symptoms, and early treatment options available for stroke with particular emphases on thrombolysis, and its window period. Materials and Methods A cross-sectional telephonic interview-based survey was conducted in the states of Punjab, Haryana, and Himachal Pradesh of North India from August 2017 to December 2017. Results Of 350 respondents those participated in the survey with a mean age ± standard deviation of 39.93 ± 13.41 years, hypertension was found to be the most common risk factor among the study participants (n = 205, 58.57%) and 28.85% of the study participants were not aware of the risk factors, whereas 46% of the participants were not aware of warning signs of stroke. The window period of thrombolysis was compared with gender, and it was found that the higher number of male participants (n = 49) adduced the right window of thrombolysis (0–4.5 hours) as compared with the female participants (n = 26). Conclusions Survey results suggested that there exists scant information about risk factors, alarming signs, and early treatment approaches among community adults toward stroke. In this regard, there is an immense need to conduct awareness camps related to stroke to educate the vulnerable public.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".