Vemurafenib and Radiosensitization
Bibliographic record
Abstract
IMPORTANCE: The BRAF inhibitor, vemurafenib, was recently approved for the treatment of patients with BRAFV600 metastatic melanoma. Wider use of this drug and longer follow-up periods of treatment are resulting in the emergence of a growing number of reports detailing new adverse effects. Cutaneous adverse effects are preeminent with UV-A-dependent phototoxicity, hyperkeratotic folliculitis, hand-foot skin reaction, hair changes, verrucous papillomas, keratoacanthomas, and squamous cell carcinomas. OBSERVATIONS: We report 2 cases of dermatitis occurring on a previously irradiated skin area in patients treated with vemurafenib for a BRAFV600-mutated metastatic melanoma. The first case occurred 10 days after a low dose of radiation was delivered that usually does not induce any radiodermatitis, suggesting radiosensitization by vemurafenib. The second case occurred 30 days after radiotherapy and was diagnosed as radiation recall dermatitis. CONCLUSIONS AND RELEVANCE: Vemurafenib should be considered a potential cutaneous radiosensitizer and an inducer of radiation recall dermatitis. However, these adverse effects are easily managed with topical corticosteroids. Dose reduction or interruption of vemurafenib is not required. Further studies and reports will enlighten us as to whether this pharmacodynamic interaction between x-rays and vemurafenib is also seen with other BRAF or MEK inhibitors on the same mitogen-activated protein kinase pathway currently under development.
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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 | 0.001 |
| 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) | 0.004 | 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".