A Review of Evidence for a Therapeutic Application of Traditional Japanese Kampo Medicine for Oral Diseases/Disorders
Bibliographic record
Abstract
Kampo medicines prescribed by specialized medical practitioners and Japanese physicians have gradually reemerged in Japan as alternatives to Western medications. Kampo formulations are composed of several plant extracts and, as such, the broad variety of phytochemicals they contain likely act synergistically to provide their beneficial effects. Kampo medicines have traditionally been prescribed for a number of health conditions, including chronic hepatitis, bronchial asthma, anemia, etc. The aim of this article is to review the beneficial effects of Kampos with respect to oral health. Pertinent papers published between 1970 and 2017 were retrieved by searching in PubMed, ScienceDirect, Web of Science, and Scopus using key words followed by evaluation of the relevant articles. In vitro studies have identified a number of properties that give credence to the potential of Kampos for treating or preventing oral diseases/disorders. Given their anti-microbial and anti-inflammatory properties, they may be promising agents for controlling periodontal diseases, oral mucositis, xerostomia, and drug-induced gingival overgrowth. Since some oral diseases have a complex etiology that involves microbial pathogens and the host immune response, agents with dual functionality such as Kampo phytochemicals may offer a therapeutic advantage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".