Implementing an eHealth Model of Care for Pediatric Patients and Families at the End of Treatment for Acute Lymphoblastic Leukemia (EMERGE): Type 2 Nonrandomized Hybrid Implementation-Effectiveness Trial Study Protocol
Bibliographic record
Abstract
Background: Despite increasing survival rates for childhood cancers, physical and psychological late effects are common. The end-of-treatment period is recognized as a complex transition period, and there are few evidence-based models of care to address patient and family needs during this early survivorship period. The EMERGE model of care has been developed to provide eHealth-delivered, multidisciplinary care to patients and families in the 12 months following treatment for acute lymphoblastic leukemia, the most common type of pediatric cancer. Objective: The primary aim of this study is to assess the implementation success of the EMERGE model of care into the clinical setting. Secondary aims include evaluating effectiveness and cost consequences. Methods: The study uses a nonrandomized hybrid implementation-effectiveness design, assessing both implementation and clinical outcomes. Implementation metrics include evaluating the reach, acceptability, feasibility, and maintenance of the EMERGE model. Clinical effectiveness outcomes include parent satisfaction with the EMERGE intervention and pre-post intervention evaluation of parent psychological stress and unmet information needs. The Reach, Effectiveness, Adoption, Implementation, and Maintenance implementation science framework was used to guide study outcomes. Semistructured interviews with clinicians and parents will further evaluate the acceptability and sustainability of the EMERGE model and appraise barriers and facilitators to implementation. Cost analysis will include evaluation of the resources required for program delivery and the impact on subsequent health care service use measured using Medicare data and health service usage collected during the EMERGE intervention. Results: The trial commenced in December 2022, and recruitment concluded in October 2025, with 81 families recruited. Data collection is ongoing and is anticipated to be completed in Summer 2026. Conclusions: This study will address a critical gap in multidisciplinary care delivery at the end of treatment for young survivors of acute lymphoblastic leukemia and their families. The EMERGE model has the potential to improve the quality of life of patients and families by providing an early survivorship intervention. Importantly, the usage of an eHealth (telehealth) model will enable distance-delivered care, facilitating family participation regardless of geography. By measuring implementation, clinical, and cost impacts, this study will inform the future development of end-of-treatment models of care that are almost universally lacking in pediatric oncology care.
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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 | 0.028 | 0.027 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.008 |
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".