Topical Application of Hangeshashinto (TJ-14) in the Treatmentof Chemotherapy-Induced Oral Mucositis
Bibliographic record
Abstract
BACKGROUND: The optimal treatment of chemotherapy-induced oral mucositis is not well established. A recent study showed that hangeshashinto (TJ-14) might be useful for periodontal disease via downregulating pro-inflammatory prostaglandins in the cyclooxygenase pathway in human. Our study aimed to determine whether TJ-14 is effective in the management of chemotherapy-induced oral mucositis. METHODS: Fourteen patients afflicted with chemotherapy-induced oral mucositis during mFOLFOX6 or FOLFIRI treatment for metastasis of advanced colorectal cancer were randomly assigned to topical TJ-14 treatment thrice daily for 7 days. Patients prepared a 50 ml solution with 2.5 g of TJ-14 dissolved in tap water and rinsed their oral mucosa for more than 5 seconds and then expectorated it. TJ-14 was also topically applied with a cotton pellet on the mucosal lesions. The severity of oral mucositis was evaluated using the Common Terminology Criteria for Adverse Events version 4 before and after one-week TJ-14 treatment. RESULTS: = 0.0012). Compared to baseline, none of the patients' CTCAE grades worsened. The compliance of TJ-14-treatment was good and side effects from TJ-14 were not observed. CONCLUSIONS: Topical application of TJ-14 may have therapeutic effects in patients with chemotherapy-induced oral mucositis via downregulation of pro-inflammatory prostaglandins. A prospective, randomized, controlled, double-blind studies are necessary to confirm the findings of this open-label, pilot study.
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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 | 0.000 |
| 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) | 0.001 | 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".