Optimizing childhood oncology care transition from pediatric to adult settings: A survey of primary care physicians’ and residents’ perspectives
Bibliographic record
Abstract
PURPOSE: The majority of childhood cancer survivors suffer from late adverse effects after the completion of treatment. The prospect of most survivors reaching middle-age is a relatively new phenomenon, and the ways by which current and future primary care physicians (PCPs) will address this novel public health challenge are uncertain. METHODS: A survey assessing knowledge level and information delivery preferences regarding long-term follow-up guidelines for adult patients having survived a childhood cancer was distributed by e-mail through the Quebec (Canada) national associations of PCPs and residents (n=238). RESULTS: Participants reported an estimated average of 2.9 ± 1.9 cancer survivors in their yearly caseload, and only 35.3% recalled having provided services to at least one survivor in the last year. Most participants indicated ignoring validated follow-up guidelines for these patients (average score 1.66 on a Likert scale from "1-totally disagreeing" to "5-totally agreeing"). Scarce access to personalized follow-up guidelines and lack of clinical exposure to cancer survivors were identified as main obstacles in providing optimal care to these patients (respective averages of 1.66 and 1.84 on a Likert scale from "1- is a major obstacle" to "5-is not an obstacle at all"). CONCLUSION: The PCPs and residents rarely provide care for childhood cancer adult survivors. On an individual basis, there is a clear need for increased awareness, education and collaboration regarding long-term care of childhood cancer adult survivors during medical training. On a more global basis, structural, organizational and cultural changes are also needed to ensure adequate care transition.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".