Cardiovascular Disease Risk Factors among White-Collar Workers towards Healthy Communities in Malaysia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the most common cause of mortality worldwide, including in most Western countries and Asian countries such as Malaysia. Reports by The Department of Statistics Malaysia highlighted that ischaemic heart diseases and cerebrovascular disease, which are a few of CVD, was the principal cause of death in 2016 and 2017. At the same time, big data is a part of Malaysia's fast-growing technology and has grown prominently in the six Malaysian government's public sector clustering which are profiling, social, economy, transportation, education, and also in healthcare. This paper focuses on healthcare big data, which is a prime example of how the three Vs of data, velocity (speed of generation of data), variety, and volume, are an innate aspect of the data it produces. Most healthcare data analytics has been conducted in the United States and Europe, however there were some studies in Canada and very little in Asia. This study will be conducted in Selangor, Malaysia focusing on white-collar workers among the Selangor healthy community. Interviews will be held within medical practitioner or healthcare provider in order to collected information. The information available from the National Cardiovascular Database (NCVD) published reports will be used to conduct the data analysis experiments which will lead towards the identification of CVD risk factors. The results obtained show that data crawling of social media data can be used as a means towards healthcare big data analytics. This will hence aid in the Malaysian healthcare integration process and aid the Malaysian government to provide better healthcare for the overall Malaysian healthy community and society.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it