Comprehensive long‐term follow‐up programs for pediatric cancer survivors
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to provide a detailed description of comprehensive long-term follow-up (LTFU) programs for pediatric cancer survivors. METHODS: Program directors from 24 comprehensive LTFU programs in the U.S. and Canada completed a 6-page survey that provided details in 5 categories: description of the program, perceived benefits and strengths of the program, barriers to the development and use of the program, methods to improve the program, and an ideal model of care for pediatric cancer survivors. RESULTS: Participants identified the following primary benefits to health care delivered to survivors through LTFU programs: health care delivered by clinicians familiar with long-term risks of survivors, provision of risk-based screening and surveillance for late effects, and targeted education for risk reduction and healthy lifestyles. Key barriers to the functioning of LTFU programs included system-driven and patient/survivor-driven factors. System-driven factors included inadequate resources and finances to sustain programs, low institutional commitment toward the provision of survivorship care, lack of capacity to care for the growing population of survivors, and difficulties with ongoing communication with community physicians. Survivor-driven barriers included lack of interest and lack of awareness of cancer-related risks. CONCLUSIONS: This report describes the frequency, content, and setting of follow-up care delivered by pediatric comprehensive LTFU programs. Critical challenges as survivorship care evolves will include integrating a structured process of program evaluation and building capacity for 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".