Evidence-Based Recommendations for Education Provided to Patients and Families Regarding the Adverse Events of ALK and MEK Inhibitors: A Systematic Review From the Children's Oncology Group
Bibliographic record
Abstract
Background: Pediatric oncology patients receive multiple modalities of therapy to treat their malignancies. These modalities have the potential for acute toxicity and late effects. In the last decade, a new modality known as targeted biological therapy, has become an integral part of treatment for pediatric cancers. As targeted therapy use has increased, adverse events specific to these targeted agents have emerged, requiring a new effort focused on providing education to patients and families regarding how best to report, monitor, and manage these adverse events. Method: A clinical question was developed to guide the systematic literature review. Anaplastic lymphoma kinase (ALK) and mitogen-activated protein kinase kinase (MEK) inhibitors were selected for review due to their frequency of use in pediatric oncology. The search was conducted to identify relevant articles published between January 1, 2000 and May 5, 2020. Articles were screened by two team members for inclusion/exclusion criteria using the web-based systematic review tool, Rayyan. Results: Twenty-seven articles met the eligibility criteria for inclusion and were evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation criteria. Adverse events for ALK and MEK inhibitors included manifestations of the gastrointestinal, hematologic, dermatologic, musculoskeletal, neurological, cardiovascular, and ocular systems. Recommendations for patient/family education were made for ALK and MEK inhibitors based on the reported adverse events. Conclusions: Adverse events of ALK and MEK inhibitors differ from the more common adverse events experienced with conventional treatment modalities used in pediatric oncology. It is important for nurses to include information regarding potential adverse events in patient/family education for children receiving these targeted agents.
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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 | 0.027 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".