Training and education of pediatric radiation oncologists: A survey from the 2019 Pediatric Radiation Oncology Society meeting
Bibliographic record
Abstract
To examine the educational background, clinical practice, and preferences regarding continuing medical education (CME) among radiation oncologists who attended the 2019 meeting of the Pediatric Radiation Oncology Society (PROS), a survey consisting of 20 questions was distributed asking for demographic and educational background, clinical practice, and preferences regarding pediatric radiation oncology CME. Of 188 participants, 130 (69.2%) returned the questionnaire. More than 80% reported access to CT simulation, three-dimensional radiotherapy, and general anesthesia while <30% had access to intraoperative radiotherapy, proton, and heavy particle therapy. After residency, 12.1% did further training in pediatric radiation oncology. When asked about further training in pediatrics after residency, 88.8% answered that there should be a formal training program beyond residency in order to treat children. More than 75% acquired knowledge in pediatric radiation oncology through journals, books, live meetings, and tumor boards. The results of this survey may help Pediatric Radiation Oncology Society (PROS) in creating guidelines and recommendations for improvement in pediatric radiation oncology training and practice support as well as the development of CME activities most likely to benefit practitioners.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".