A Review of Bullous Dermatologic Adverse Events Associated with Anti-Cancer Therapy
Bibliographic record
Abstract
The rapid evolution of anti-cancer therapy (including chemotherapy, targeted therapy, and immunotherapy) in recent years has led to a more favorable efficacy and safety profile for a growing cancer population, and the improvement of overall survival and reduction of morbidity for many cancers. Anti-cancer therapy improves outcomes for cancer patients; however, many classes of anti-cancer therapy have been implicated in the induction of bullous dermatologic adverse events (DAE), leading to reduced patient quality of life and in some cases discontinuation of life-prolonging or palliative therapy. Timely and effective management of adverse events is critical for reducing treatment interruptions and preserving an anti-tumor effect. Bullous DAE may be limited to the skin or have systemic involvement with greater risk of morbidity and mortality. We present the epidemiology, diagnosis, pathogenesis, and management of bullous DAE secondary to anti-cancer therapies to enable clinicians to optimize management for these patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".