Impact of Dietetic Intervention on Metabolic Syndrome Patients Attending Diet Therapy Clinic: A prospective, Single-Arm Intervention Study
Bibliographic record
Abstract
INTRODUCTION: The prevalence of metabolic syndrome (MetS) is rising globally. Dietetic intervention, as part of a multidisciplinary team approach, is increasingly being recommended for the effective management of patients with MetS. This study was designed to assess the impact of a dietetic intervention on MetS characteristics of patients attending the Diet Therapy Clinic at Tema General Hospital, Ghana. METHODOLOGY: A prospective pre-post single-arm intervention study was conducted among 168 participants who had been diagnosed with MetS and were referred to the Diet Therapy Clinic for dietetic intervention. Data on body mass index (BMI), waist circumference (WC), fasting blood glucose (FBG), high-density lipoprotein (HDL), serum triglyceride (TG), and blood pressure (BP) were collected at baseline and after three months of receiving a dietetic intervention. RESULTS: The MetS measures (BMI, WC, FBG, HDL and TG) of the patients improved at the end of the three months period (32.9 kg/m2 vs 31.7 kg/m2, p = 0.001; 101.2 cm vs 98.9 cm, p = 0.001; 11.0 mmol/L vs 7.7 mmol/L, p = 0.001; 1.1 mmol/L vs 1.2 mmol/L, p = 0.001; 2.0 mmol/L vs 1.9 mmol/L, p = 0.001 respectively). There were improvements in the mean systolic and diastolic BP values recorded after the three months (153 mmHg vs 131 mmHg, p = 0.001 and 98 mmHg vs 85 mmHg, p = 0.001 respectively). CONCLUSION: Dietetic intervention was found to have improved the MetS characteristics of patients.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".