Prophylaxis, clinical management, and monitoring of datopotamab deruxtecan-associated oral mucositis/stomatitis.
Bibliographic record
Abstract
Oral mucositis/stomatitis (hereafter stomatitis) is a common dose-limiting toxicity seen with various classes of cancer treatment. Symptoms associated with stomatitis, primarily oral pain, may impact patient quality of life and may lead to dose delay and reduction or treatment discontinuation. Datopotamab deruxtecan (Dato-DXd) is a novel trophoblast cell surface antigen 2-directed antibody-drug conjugate undergoing clinical investigation in multiple solid tumor types. Stomatitis is among the most reported adverse events associated with Dato-DXd, with most cases being grades 1-2. This article reviews the incidence of stomatitis seen with Dato-DXd, including in the phase III pivotal studies TROPION-Lung01 and TROPION-Breast01 (in patients with non-small cell lung cancer and hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer, respectively), both studies met a dual primary endpoint of statistically significant improvement in progression-free survival compared to standard-of-care chemotherapies. Developing new cancer therapies requires evidence-based strategies to successfully prevent, monitor, and manage adverse events. Accordingly, a thorough evaluation of potential underlying mechanisms, risk factors, available clinical data, and adequacy of preventive and management recommendations for stomatitis is presented here. Prophylaxis recommendations for a daily oral care plan include oral hygiene education and the use of a prophylactic steroid-containing mouthwash. Ongoing studies continue to collect data on Dato-DXd-associated stomatitis to further characterize clinical features and possible mechanisms of this toxicity. Appropriate management may reduce the incidence, duration, and severity of events, improve quality of life, and support patient adherence to treatment.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".