Abstract B080: Survivorship care models for childhood cancer survivors in low- and middle-income countries: A scoping review
Bibliographic record
Abstract
Abstract Childhood cancer survivors require specific care that includes monitoring for late effects of cancer therapy. Current knowledge on how to deliver survivorship care is based on experience in high-income countries, which may not be applicable to other settings. We thus conducted a scoping review to characterize and describe the models of survivorship care available to childhood cancer survivors in low- and middle-income countries (LMICs). Fifty-one studies were selected from a comprehensive literature search of 8 electronic and 2 grey-literature databases followed by title, abstract, and full-text screening. Elements of survivorship care were categorized within the domains of the Quality of Cancer Survivorship Care Framework using representative Children’s Oncology Group Long-Term Follow-Up Guidelines for Survivors of Childhood, Adolescent, and Young Adult Cancers guidelines for childhood cancer survivors. The Quality of Cancer Survivorship Care Framework is comprised of 5 domains related to survivorship care services, and 4 domains related to health care delivery. From the 51 studies chosen for data extraction, 61 LMICs were identified. Of these 61 LMICs, only 11 countries reported having active survivorship care services. The most reported services in the included papers from LMICs are related to the surveillance and management of chronic medical conditions and psychosocial effects. Of the countries that that reported survivorship services, literature from India reported on the four fundamental health care delivery domains identified by the Quality Cancer Survivorship Care Framework, which include clinical structure, communication and decision-making, care coordination, and patient/caregiver experience. Additionally, 8 of the 11 countries publishing about their survivorship care documented access to specialty medical care and healthcare professionals for childhood cancer survivors, such as onco-endocrinologists and psychologists. There are patient, clinician, and health system-related barriers to developing and implementing survivorship care in LMICs. Efforts to optimize the delivery of survivorship care include increased patient and family education about treatment late effects, optimized healthcare provider staffing, staff education, and training, and implementation of standardized practice guidelines that are resource-stratified and therefore applicable in multiple settings. Citation Format: Celine Lecce, Avram E. Denburg, Sumit Gupta, Alexandra Martiniuk, Ariel Qi. Survivorship care models for childhood cancer survivors in low- and middle-income countries: A scoping review [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B080.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".