Reduced Dental Plaque Formation in Dogs Drinking a Solution Containing Natural Antimicrobial Herbal Enzymes and Organic Matcha Green Tea
Bibliographic record
Abstract
The results of an exploratory, multicenter clinical study confirmed the hypothesis that a novel, natural, and safe oral care product (OCP) reduced the rate of plaque formation on teeth of dogs consuming the OCP (antimicrobial plant-derived enzymes, organic matcha green tea, cultured dextrose, sodium bicarbonate, and ascorbic acid) compared to controls. Healthy dogs without periodontitis, of varying breeds, sex, and age, were recruited and enrolled, using nonrandomized stratification methods, into a control and treatment groups. Treatment group dogs drank only water into which OCP was suspended, for 28 days. Control group dogs drank their normal household water. On day 0 all teeth were cleaned by a veterinarian and gingivitis was assessed. On days 14, 21, and 28 plaque index, plaque thickness, gingivitis, freshness of breath, and general health were assessed. Over the 28 days of study, dogs on the OCP had significant reduction in plaque index and plaque thickness compared to controls. By day 14 OCP reduced plaque formation by 37%; the 28-day reduction in plaque index and coverage averaged 22% with no measurable gingivitis or calculus. Conclusion . Using the OCP attenuated dental plaque formation when consumed as normal drinking water and in the absence of other modes of oral care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".