Physician needs and preferences for information about long-term follow-up and care of survivors of childhood cancer.
Bibliographic record
Abstract
66 Background: The treatments that childhood cancer survivors (CCS) undergo can lead to an increased risk of other health problems later in life, and risk-based follow-up care is necessary throughout their lifetime. Regrettably, family physicians caring for CCS often report having no record of their patients’ past disease, treatment history, or need for follow-up. As the large majority of CCS will transition from specialized pediatric oncology care to generalized adult care in the community, detailed treatment summaries and Survivorship Care Plans (SCP) are needed to ensure that both the survivor and their care providers are adequately informed of the survivors diagnosis, treatment(s), potential risk for late effects, and long-term surveillance and healthcare needs. This qualitative study sought to explore the needs, preferences, and the perceived utility of SCP for family physicians (FP) caring for CCS. Methods: Using publically available Children’s Oncology Group guidelines, a de-identified sample SCP indicating the diagnosis, treatment(s) received, and follow-up recommendations for a common childhood cancer diagnosis was automatically generated using a newly developed algorithm and patient data from the Cancer in Young People – Canada registry. Semi-structured telephone interviews with six FP that have a known CCS in their practice were then used to gain insight into the FP perceived role in the long-term management and care of CCS, their cancer information needs, concerns with communication, their perceived utility of the SCP, and preferred format(s) for receiving the SCP. The constant comparative method was used for thematic analysis. Results: The key themes emerging from the six completed interviews include a lack of confidence among FP in their ability to care for CCS and a need for additional knowledge and resources. FP also identified psychological barriers to cancer care discussions with CCS and identified a patient need for greater psychological support. Overall, SCP were viewed as helpful. Conclusions: SCP are perceived to be of benefit to FP of CCS as they serve to increase their knowledge of patient diagnosis, treatments received, and suggested risk-based follow-up.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".