The Effect of Lemon, Watermelon, and Cucumber Infused Water to Decrease Blood Pressure
Bibliographic record
Abstract
Introduction: Hypertension "The Silent Killer" is a serious medical condition that can increase the risk of heart, brain, and kidney disease. The preventive role of nurses in clients with uncontrolled hypertension to overcome complications can be done by adopting a healthy lifestyle. A healthy lifestyle can be given infused water therapy with cucumber, lemon, and watermelon. Objective: The purpose of this study was to analyze the effect of lemon, watermelon, and cucumber-infused water on reducing blood pressure in clients with hypertension. Method: This study used a Quasi Experiment design with a Nonequivalent Control Group design. Sampling using a purposive sampling technique. Hypothesis testing is used with two different tests mean independent sample t-test and dependent sample t-test. Results: There were differences before and after the intervention of Lemon, Watermelon, and Cucumber Infused Water to reduce blood pressure in clients with hypertension (ρ 0.030; 0.000; 0.000 <0.05). Conclusion: Consumption of infused water of lemon, watermelon, and cucumber can lower blood pressure. Of the three ingredients, the highest blood pressure reduction was used cucumber-infused water.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".