Models of Care for Survivors of Childhood Cancer From Across the Globe: Advancing Survivorship Care in the Next Decade
Bibliographic record
Abstract
With improvements in cancer treatment and supportive care, a growing population of survivors of childhood cancer at risk for significant and potentially life-threatening late effects has been identified. To provide a current snapshot of the models of care from countries with varying levels of resources and health care systems, stakeholders in childhood cancer survivorship clinical care and research were identified from 18 countries across five continents. Stakeholders responded to a survey and provided a brief narrative regarding the current state of survivorship care. Findings indicate that among pediatric-age survivors of childhood cancer (allowing for differences in age cutoffs across countries), resources are generally available, and a large proportion of survivors are seen by a physician familiar with late effects in most countries. After survivors transition to adulthood, only a minority are seen by a physician familiar with late effects. Despite the need to improve communication between pediatric oncology and primary care, only a few countries have existing national efforts to educate primary care physicians, although many more reported that educational programs are in development. These data highlight common challenges and potential solutions for the lifelong care of survivors of childhood cancer. Combining risk-based and patient-oriented solutions for this population is likely to benefit both providers and patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".