Physician, patient, and caregiver support for a formal certification in pediatric neuro-oncology: A survey-based report from the SNO pediatrics working group
Bibliographic record
Abstract
Background: Although CNS tumors are the most common pediatric cancer in the United States, most physicians caring for these patients are not formally certified in the subspecialty. To determine support for developing a formal certification process in pediatric neuro-oncology, the Society for Neuro-Oncology's Pediatrics Special Interest Track Training and Credentialing working group performed a cross-sectional survey-based study of physicians and patients/caregivers of children with a CNS tumor history. Methods: Surveys were built in Survey Monkey and were available for 3 months. The physician survey had 34 questions and was open to doctors currently caring for pediatric neuro-oncology patients. The patient/caregiver survey had 13 questions. Both surveys were completed anonymously. Results: The physician survey was completed by 193 participants, the majority of whom self-identified as oncologists. Only 5.6% of survey participants had ever been board-certified in neuro-oncology; the majority of participating physicians were either unaware that this certification existed or thought they were not eligible due to training in pediatrics rather than neurology or internal medicine. Almost half of the self-identified pediatric neuro-oncologists had not completed any specific clinical neuro-oncology training. Over 75% of physicians were supportive of the implementation of a formal certification process in pediatric neuro-oncology. A total of 30 participants completed the patient/caregiver survey. Although the majority of survey participants were highly satisfied with their oncologist, 70% would have been more comfortable if their oncologist had been specifically certified in pediatric neuro-oncology. Conclusions: There is support from physicians, patients, and caregivers to establish a formal certification process in pediatric neuro-oncology.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".