Commonly Reported Adverse Events Associated With Pediatric Immunotherapy: A Systematic Review From the Children’s Oncology Group
Bibliographic record
Abstract
BACKGROUND: Immunotherapy is a new and promising approach to treating pediatric cancers. These types of therapies have unique mechanisms of action for identifying and fighting cancer, as compared with traditional chemotherapy, and therefore are associated with different therapy-related adverse events (AEs). The purpose of this systematic review was to review available evidence to: (a) identify commonly reported AEs associated with immunotherapy agents frequently used in pediatric oncology and (b) generate recommendations for nursing practice. METHOD: A clinical question was developed and used to guide the systematic literature review. Five immunotherapy agents (dinutuximab, blinatumomab, rituximab, inotuzumab ozogamicin, brentuximab vedotin) were selected for inclusion secondary to their high relevance to pediatric oncology. A literature search was conducted to locate articles published between January 1, 2003 and October 31, 2018. RESULTS: Seventeen articles met eligibility criteria for inclusion and were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation criteria. The most commonly reported AEs for the selected immunotherapy agents were identified and summarized. Strong recommendations are made for nurses to become familiar with the unique AE profiles associated with individual immunotherapy agents. Agent-specific recommendations for nursing practice regarding AEs associated with dinutuximab and rituximab were generated. CONCLUSIONS: Immunotherapy is rapidly emerging as an effective therapy for pediatric cancers. Nurses need to be aware of the breadth of agent-specific, immunotherapy-related AEs to appropriately monitor and manage patients receiving these therapies. Additional work is needed to confidently profile immunotherapy-related AEs in pediatric oncology and to develop agent-specific educational materials for patients/families.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".