Targeted Therapy– and Chemotherapy-Associated Skin Toxicities: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PROBLEM IDENTIFICATION: Preventing and managing skin toxicities can minimize treatment disruptions and improve well-being. This systematic review aimed to evaluate the effectiveness of interventions for the prevention and management of cancer treatment-related skin toxicities. LITERATURE SEARCH: The authors systematically searched for comparative studies published before April 1, 2019. Study selection and appraisal were conducted by pairs of independent reviewers. DATA EVALUATION: The random-effects model was used to conduct meta-analysis when appropriate. SYNTHESIS: 39 studies (6,006 patients) were included; 16 of those provided data for meta-analysis. Prophylactic minocycline reduced the development of all-grade and grade 1 acneform rash in patients who received erlotinib. Prophylaxis with pyridoxine 400 mg in capecitabine-treated patients lowered the risk of grade 2 or 3 hand-foot syndrome. Several treatments for hand-foot skin reaction suggested benefit in heterogeneous studies. Scalp cooling significantly reduced the risk for severe hair loss or total alopecia associated with chemotherapy. IMPLICATIONS FOR RESEARCH: Certainty in the available evidence was limited for several interventions, suggesting the need for future research. SUPPLEMENTAL MATERIAL CAN BE FOUND AT HTTPS: //onf.ons.org/supplementary-material-targeted-therapy-and-chemotherapy-associated-skin-toxicity-systematic-review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".