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.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".