Interventions to Promote Oral Medication Adherence in the Pediatric Chronic Illness Population: A Systematic Review From the Children’s Oncology Group
Bibliographic record
Abstract
Pediatric oncology protocols frequently include multiple oral medications administered at varied dosing schedules, often for prolonged periods of time. Nonadherence to protocol-directed oral medications may place patients at increased risk for morbidity and mortality. The purpose of this systematic review was to evaluate the existing body of evidence to determine best-practice recommendations regarding interventions for oral medication adherence in children and adolescents with cancer. Twenty-four articles were systematically reviewed and evaluated according to the Grading of Recommendations, Assessment, Development, and Evaluation criteria; 2 studies focused on the pediatric oncology population, and the remaining 22 studies focused on other chronic illnesses of childhood. A variety of interventions to increase oral medication adherence in children were identified, including pill swallowing, technology, incentivization, education-based intervention, psychosocial support-based intervention, and combination intervention. Most interventions were shown to have some benefit in pediatrics, most in the non-oncology setting. The overall synthesis of the literature indicates that nonadherence to oral medications is a prevalent problem in pediatrics, and much work is needed to address this problem, particularly in pediatric oncology.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".