Online pediatric oncology education: A novel teaching tool for oncology residents
Bibliographic record
Abstract
9048 Background: Deficiencies in pediatric oncology training for residents have been identified internationally. A lack of training in pediatric oncology during residency is correlated with impaired outcomes for pediatric patients. There is a clear need for novel teaching tools to supplement training in pediatric oncology. Purpose: The goal of this project was to develop a series of multimedia web-based learning modules for the interactive instruction and review of pediatric oncology. A needs-based assessment of pediatric oncology training in Canadian radiation oncology residency programs was used to guide web-site development. Methods: A needs-based analysis was conducted via a web-based survey sent to all Canadian radiation oncology program directors and residents. The survey was designed to identify the limitations of current training curriculum in pediatric oncology. A multidisciplinary team, including pathologists, radiologists, medical physicists, radiation and medical oncologists, developed the web site curriculum. The site was designed using teaching modules to focus on pediatric tumour sites. Modules were developed using a common format, divided into chapters detailing specific tumour types. Each chapter reviewed relevant epidemiology, anatomy, pathology, radiology, and treatment for the tumor type. A self-assessment tool was included at the end of the modules. User satisfaction surveys were used for feedback. Results: 100% (10/10) of Canadian radiation oncology program directors and 66% (69/106) of Canadian residents responded to the survey. 56% (59/106) of resident respondents felt that teaching in pediatric radiation oncology was inadequate and 56% (20/36) of senior residents felt unprepared for questions in pediatric oncology on qualifying board exams. 90% (105/116) of respondents felt that a web-based teaching tool would be useful. The first module, pediatric neuro-oncology, has been completed. Results indicate users find this module useful for supplementing traditional training. Conclusions: There are no other interactive teaching tools in pediatric oncology. This unique web-based educational program will allow self-directed study in the area of pediatric oncology, providing more effective learning of this challenging yet essential area of study. No significant financial relationships to disclose.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".