Expert consensus on disease-based long-term follow-up care plans for childhood cancer survivors
Bibliographic record
Abstract
BACKGROUND: Childhood cancer survivors (CCSs) are at increased risk of long-term treatment-related complications. Although international guidelines support risk-based long-term follow-up (LTFU) care, its standardized implementation in China has been limited. To address this gap, the National Children's Medical Center-Shanghai convened a multidisciplinary expert panel to develop disease-based LTFU care plans tailored to the Chinese healthcare context. METHODS: Guided by established international frameworks (Children's Oncology Group, International Guideline Harmonization Group, and PanCareFollowUp), an expert group representing 25 institutions across China developed consensus-based LTFU care plans for common pediatric cancer patients and post-hematopoietic cell transplant survivors. Each care plan includes core components: a treatment summary, risk stratification for late effects, recommended surveillance, psychosocial evaluation, and lifestyle guidance. The panel also developed a consensus on the specific roles of oncologists, primary care providers, and subspecialists. RESULTS: Finalized care plans provide structured, risk-adapted follow-up pathways for CCSs. The model emphasizes multidisciplinary collaboration, clinical feasibility, and scalability across diverse settings. As part of the care process, a centralized survivorship database has been integrated to facilitate clinical use and data collection. This system supports the generation of standardized treatment summaries and longitudinal documentation of late effects across the continuum of survivorship care. Tools, such as clinician checklists and survivor education templates, were also developed to support clinical use and promote consistency across institutions. A list of outcome metrics was proposed to evaluate the implementation outcomes of this initiative. CONCLUSIONS: This expert consensus establishes an innovative, nationally coordinated, disease-specific LTFU care framework for CCSs in China. This study provides a practical foundation for improving survivorship care quality and guiding clinical practice nationwide. This model can serve as a blueprint for other low- and middle-income countries seeking to strengthen LTFU care for CCSs.
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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 | 0.091 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".