Cardiovascular Disease-Related Lifestyle Factors among People with Type 2 Diabetes in Pakistan: A Multicentre Study for the Prevalence, Clustering, and Associated Sociodemographic Determinants
Bibliographic record
Abstract
Background. We evaluated the prevalence and clustering pattern of cardiovascular disease (CVD) related lifestyle factors and their association with CVD among patients with type 2 diabetes. We also examined the association of these factors with various socio-demographic characteristics. Methods. A total of 1000 patients with type 2 diabetes were interviewed in a cross-sectional, multi-center study in out-patient clinics in Karachi, Pakistan. Results. In this study 30.3% study participants had CVD. Majority of the patients were physically inactive and had adverse psychosocial factors. Forty percent of the study participants were exposed to passive smoking while 12.7% were current smokers. Only 8.8% of study subjects had none of the studied lifestyle factor, 27.5% had one, while 63.7% had two or three factors. CVDs were independently associated with physical inactivity, adverse psychosocial factors, passive smoking and clustering of two or three lifestyle factors. Physical inactivity was more prevalent among females and patients with no/less education. Proportion of adverse psychosocial factors were higher among females, elders and patients with no/less education. Clustering of these lifestyle factors was significantly higher among females, elderly and no/less educated patients. Conclusion. These results suggest the need of comprehensive and integrated interventions to reduce the prevalence of lifestyle factors.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".