Effect of Tomato and Cucumber Juice on Blood Pressure in Hypertensive Patients: a Quasi-Experimental Study
Bibliographic record
Abstract
Background: Hypertension is associated with the improvement of cardiovascular disease and all-cause mortality. A healthy diet based on consuming natural foods can prevent and control hypertension. Objective: The aim of this study was to analyze the effectiveness of tomato and cucumber juice in reducing the blood pressure of hypertensive patients. Methods: The study used a quasi-experiment pretest-posttest control group design. The target population are people with hypertension Stage 1, people living in urban area - Cibiru Health Centre Work Area. Purposive sampling was used and the sample size was calculated using the average comparison formula with effect size=0.9, α=0.05, β =0.2. Forty-five subjects involved in the study were divided into three groups (15 subjects were given tomato juice, 15 subjects were given cucumber juice, and 15 subjects were given treatment with mineral water (control group)., This research used 100 grams of ripe red tomatoes, 100 grams of fresh cucumber, and 200 ml of water. The data collected were patient characteristics and blood pressure. The ANOVA analysis test and the Bonferroni Post Hoc test were used to analyze the data. Results: The results of the study showed a difference in blood pressure reduction in each group. The decrease in systolic blood pressure in the tomato juice group was 7.3±3.1, the cucumber juice group was 4.2±3.3, and the control group was -0.0±2.5 (p=0.0001). The decrease in diastolic blood pressure in the group given tomato juice was 9.2±3.1, the group given cucumber juice was 7.6±3.4, and the control group was 0.4±2.1 (p=0.0001). Conclusion: There is a difference in blood pressure reduction between the group given tomato juice and the control group, and there is a difference in blood pressure between the group given cucumber juice and the control group.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".