Doxepin rinse for management of mucositis pain in patients with cancer: one week follow‐up of topical therapy
Bibliographic record
Abstract
This study assessed the effectiveness of oral doxepin rinse for mucositis-related pain management in patients following 1 week of repeated dosing. Patients with oral mucositis due to head and neck radiation therapy or hematopoietic stem cell transplant (HSCT) were recruited to participate in a 1-week follow-up study. Subjects who gave informed consent rinsed with doxepin (5 ml) during the initial visit and were then told to use doxepin rinse over the next week as needed, three to six times per day, and return for a follow-up visit. At each visit, mucositis was scored using the Oral Mucositis Assessment Scale and oral pain was assessed using a visual analogue scale before and after rinsing. The use of a systemic analgesic was recorded, and side effects were documented. At the follow-up visit, subjects were also asked to retrospectively report average pain scores they experienced over the past week, 5 and 15 minutes following rinse. Nine subjects were enrolled in the study. Statistically significant reductions in pain scores were reported for 2 hours following doxepin rinse during the initial visit (p < .05). Patients recalled that their pain significantly dropped within 5 minutes of rinsing over the week of repeated dosing (p < .05). At the follow-up visit, subjects reported statistically significant pain reduction 5 minutes after doxepin rinsing (p < .05). These results indicate that doxepin rinsing continues to produce reduced intensity of pain levels over a 1-week span of repeated dosing.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| 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".