Abstract P567: Cardiovascular Health Awareness and Risk Assessment in Schoolteachers in Calcutta, India
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is the leading cause of death in India, surpassing all infectious causes. Early awareness of CVD and its risk factors and adoption of heart healthy lifestyle practices are crucial to reducing the impact of CVD in the community. Objective: To assess schoolteachers’ awareness of CVD and to compare the calculated ten-year risk of heart attack, stroke, and death by the Atherosclerotic Cardiovascular Disease (ASCVD) model to the observed event rates. Methods: We surveyed 4,150 schoolteachers out of 5,321 (78% response rate) from 400 schools in Calcutta during 2019 using a questionnaire assessing their awareness of CVD in five domains: prevalence, spectrum of CVD, its nature, risk factors, and benefits of a heart healthy lifestyle. The study population was stratified by risk tertile of their 10-year calculated ASCVD score. Results: Awareness about cardiovascular health among schoolteachers (male 41%, mean age 44 years) was low: 33 (mean score out of maximum 100), varying in different domains as shown below. Although most (86%) were at mild risk for CVD with ASCVD scores below five, nine percent had moderate risk, and five percent had high risk of CVD. There was no significant association between their risk score and their awareness of CVD health (correlation coefficient r = - 0.022, 95% confidence interval - 0.052, 0.009). Conclusions: Cardiovascular health awareness among schoolteachers in Calcutta is suboptimal, many of whom are at risk for CVD. Awareness of the disease, detection of risk factors at an early stage, and adoption of a healthy lifestyle may help reduce CVD in this community.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".