Association of Coronary Risk Factors with BMI, Blood Pressure, and Diabetic Score in Football Factory Workers
Bibliographic record
Abstract
Background: Cardiovascular diseases are the leading cause of mortality worldwide. Occupational activities may influence coronary risk factors such as BMI, blood pressure, and diabetes, particularly among sedentary workers.Objective: This study aimed to evaluate the association of coronary risk factors with BMI, blood pressure, and diabetes risk score among football factory workers.Methods: A cross-sectional study was conducted among 102 football factory workers aged 40–55 years in Sialkot, Pakistan. Data were collected using the Physical Activity Readiness Questionnaire (PAR-Q), the Finnish Diabetes Risk Score (FINDRISC), and a coronary risk table proposed by the Michigan Heart Association. Descriptive and inferential statistics, including Pearson correlation, were performed using SPSS version 25.Results: Among participants, 49.02% had high blood pressure, while 47.06% were overweight (BMI >24.5). Coronary risk was low (<5%) in 76.47% of workers, with 6.66% showing a high risk (>20%). Diabetes risk was low (<7 points) in 63.73% of workers. A weak positive correlation was observed between coronary risk and BMI (r = 0.200, p = 0.044), but correlations with blood pressure (r = 0.075, p = 0.455) and diabetes (r = 0.104, p = 0.297) were not statistically significant.Conclusion: Football factory workers demonstrated minimal coronary risk due to their relatively healthy lifestyle. BMI was the most significantly associated factor among the measured variables.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".