Cutaneous toxicities of new targeted cancer therapies: must know for diagnosis, management, and patient-proxy empowerment
Bibliographic record
Abstract
The world of oncology treatment is rapidly changing, and with the investigation into and utilization of molecular signaling pathways for cancer treatment, many new targeted small-molecule oral agents have been introduced as therapies, with more new drugs appearing every year. These agents, while generally considered less toxic overall than traditional chemotherapy, are not without adverse effects. The authors undertook an extensive literature search to determine the incidence, severity, and management strategies for small-molecule oral targeted agents approved by the FDA between 2013 and 2018. Dermatologic adverse effects are among the most frequently seen with many of these targeted therapies, and may include rashes, palmar-plantar dysesthesia, alopecia, secondary skin malignancies, and hair and nail changes. Rarely, more severe cutaneous toxicities are seen, such as Stevens-Johnson Syndrome and toxic epidermal necrolysis. In many cases, there is no specific management strategy suggested in the literature for these toxicities, but frequent monitoring of the skin, prophylactic management of palmar-plantar dysesthesia, use of corticosteroids and/or antihistamines, and intervention with dose interruption are suggested depending on circumstance and severity. Patient education and timely intervention is warranted in order to ensure that patient treatment is optimized.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".