Introduction of Exercise for Medicine (Exfome) Programme Among Patients In Primary Health Clinics In Malaysia
Bibliographic record
Abstract
INTRODUCTION: Diseases due to unhealthy lifestyles such as heart diseases and diabetes are increasing in Malaysia. Heart diseases are the leading cause of deaths of patients in government hospitals, i.e. 16.1% of total deaths in 2009. A customised exercise regime called Exercise for Medicine (EXFOME) was introduced to improve and prevent chronic diseases such as diabetes, heart disease, obesity and hypertension. MATERIAL & METHODS: A total of 126 participants from government health clinics in three states of Peninsular Malaysia were selected to participate in the EXFOME. Each participant was evaluated prior to the program using an assessment protocol. Fitness assessment and evaluation with body composition measurement were taken. Exercise therapist prescribed a personalized exercise program according to conditions. RESULTS: Effect of exercise was measured in terms of improvement in hypertension and diabetes, body weight, body fat, lipid profile and physical fitness at three and six months. CONCLUSION: EXFOME is beneficial to improve the status of hypertension, diabetes, body weight and lipid profile when it is carried out for longer period.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".