Plant based dietary supplement increases urinary pH
Bibliographic record
Abstract
BACKGROUND: Research has demonstrated that the net acid load of the typical Western diet has the potential to influence many aspects of human health, including osteoporosis risk/progression; obesity; cardiovascular disease risk/progression; and overall well-being. As urinary pH provides a reliable surrogate measure for dietary acid load, this study examined whether a plant-based dietary supplement, one marketed to increase alkalinity, impacts urinary pH as advertised. METHODS: Using pH test strips, the urinary pH of 34 healthy men and women (33.9 +/- 1.57 y, 79.3 +/- 3.1 kg) was measured for seven days to establish a baseline urinary pH without supplementation. After this initial baseline period, urinary pH was measured for an additional 14 days while participants ingested the plant-based nutritional supplement. At the end of the investigation, pH values at baseline and during the treatment period were compared to determine the efficacy of the supplement. RESULTS: Mean urinary pH statistically increased (p = 0.03) with the plant-based dietary supplement. Mean urinary pH was 6.07 +/- 0.04 during the baseline period and increased to 6.21 +/- 0.03 during the first week of treatment and to 6.27 +/- 0.06 during the second week of treatment. CONCLUSION: Supplementation with a plant-based dietary product for at least seven days increases urinary pH, potentially increasing the alkalinity of the body.
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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) | 0.002 | 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".