Impact of shared care program in follow‐up of childhood cancer survivors: An intervention study
Bibliographic record
Abstract
BACKGROUND: With the growing rate of childhood cancer cure and the risks of sequelae, long-term follow-up (FU) of survivors is a central issue. Several models have been proven far from satisfactory. Shared care FU is the result of collaboration between general practitioners (GPs) and cancer centers. We sought to demonstrate the feasibility of setting up a shared care program based on the patient-centered education of GPs and to evaluate the impact of this model in an intervention study. METHODS: We compared the FU care achievement in two childhood cancer survivor cohorts in the same pediatric oncology center, (i) control group (n = 134) and (ii) intervention study cohort (n = 137), after setting up the program. RESULTS: The rate of survivors answering the survey and the rate of patients involved in FU by their GPs were higher in intervention study cohort than in baseline one (132/137 vs. 72/134 and 110/132 vs. 13/72; P ≤ 0.0001). The lack of any FU was definitely lower (10/132 vs. 18/72; P = 0.001) in the intervention study cohort. CONCLUSION: In this shared care program, survivors overcame distrust in their GP's knowledge and entered the FU program after their GPs had been involved in patient-centered education. Personalized and incentive-based guidance was very useful in helping survivors to adhere to FU. Support of a dedicated long-term FU team was very useful. A nationwide organization, consideration of special needs in subgroups of survivors and sustained funding are needed to adjust the program in the very long term.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".