Longitudinal needs and cancer knowledge in Swiss childhood cancer survivors transitioning from pediatric to adult follow-up care: results from the ACCS project
Bibliographic record
Abstract
PURPOSE: Childhood Cancer Survivors (CCSs) have an increased risk for treatment-related chronic health conditions, but the adherence to long-term follow-up (LTFU) care decreases over time. We therefore assessed the CCSs' development of cancer knowledge, cancer worries, self-management skills, and expectations for LTFU care in a structured, cancer center-based transition model-a crucial part for maintaining adherence. METHODS: Using questionnaire-based surveys, we compared the CCSs' cancer knowledge with medical record data and assessed cancer worries (6 questions), self-management skills (15 questions), and expectations (12 questions) longitudinally by validated scales. We used descriptive statistics for presenting our results. RESULTS: We analyzed 17 CCSs, 71% were female, had a median age of 8 years at diagnosis and 21 years at study enrollment. The knowledge about late effects increased during the transition process, except for the risk of secondary malignancies. Leukemia survivors had a decrease in cancer worries. At least 75% of the CCSs agreed to 11 of 15 self-management questions before and after transition, with the highest increase over time in less parental involvement. The CCSs expected the most, that physicians know the CCSs' cancer history, that the visit starts on time, and that physicians can always be called in case of questions. CONCLUSIONS: Our transition model improved cancer knowledge, especially the risk for late effects, decreased cancer worries, and identified expectations for LTFU care which should be considered in the future. A structured transition process with evidence-based tools further increases the knowledge of CCS for LTFU through empowerment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".