Benzydamine HCl for prophylaxis of radiation-induced oral mucositis
Bibliographic record
Abstract
BACKGROUND: Benzydamine was evaluated in patients with head and neck carcinoma for treatment of radiation-induced oral mucositis, a frequent complication of radiation therapy (RT) for which there is no predictable therapy or preventive treatment currently available. METHODS: The safety and efficacy of 0.15% benzydamine oral rinse in preventing or decreasing erythema, ulceration, and pain associated with oral mucositis during RT were evaluated in a randomized, placebo-controlled trial conducted in patients with head and neck carcinoma. Subjects were to rinse with 15 mL for 2 minutes, 4-8 times daily before and during RT, and for 2 weeks after completion of RT; study evaluations were conducted before RT and routinely thereafter up to 3 weeks after RT. RESULTS: During conventional RT, regimens up to cumulative doses of 5000 centigrays (cGy) benzydamine (n = 69) significantly (P = 0.006) reduced erythema and ulceration by approximately 30% compared with the placebo (n = 76); greater than 33% of benzydamine subjects remained ulcer free compared with 18% of placebo subjects (P = 0.037), and benzydamine significantly delayed the use of systemic analgesics compared with placebo (P < 0.05). Benzydamine was not effective in subjects (n = 20) receiving accelerated RT doses (> or = 220 cGy/day). The incidence of adverse events between treatment groups was comparable without significant differences. Early discontinuation because of adverse events occurred in 6% of benzydamine subjects and 5% of placebo subjects, and there was 1 death (related to the primary diagnosis) in a placebo subject. CONCLUSIONS: Benzydamine oral rinse was effective, safe, and well tolerated for prophylactic treatment of radiation-induced oral mucositis.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".