Creation and Pilot-testing of Virtual Patients for Learning Oncologic Emergency Management
Bibliographic record
Abstract
Purpose or objective Management of oncologic emergencies becomes critical at the start of the second year of a radiation oncology residency. Considering the limited exposure to oncology in the medical school curriculum, this knowledge gap needs to be filled prior to managing real patients. The aim of this project was to create virtual patients (VPs) to ease this transition and improve learner readiness for independently managing oncologic emergencies on call. Material and methods A curriculum mapping exercise was done to identify gaps. The main oncologic emergencies that needed to be addressed were selected for development of the modules. Review of the key concepts for management was elucidated and validated. These included history, physical examination, imaging interpretation, staging, as well as anatomy, epidemiology, pertinent literature, differential diagnosis, prognostication, radiation treatment planning, summarizing, and patient- and peer-communication skills. Clinical vignettes were then designed, in collaboration with a virtual patient education expert, to mimic the clinical presentation and evolution of a typical patient for three common oncologic emergencies: spinal cord compression, superior vena cava syndrome, and tumor-induced hemorrhage. Results Three virtual modules were developed: spinal cord compression, superior vena cava syndrome, and tumor-induced hemorrhage. Each case included 25 to 30 vignettes that participants progressed through, with a total estimated completion time of 30 to 45 minutes. Each node branched out to provide a detailed answer and explanation of the key concept. Figures were included to mimic real patients and to provide a more authentic learning experience. The modules also included quantitative pre- and post-testing assessments, including multiple-choice questions, true or false, fill in the blank, short answers, and text response. The cases were then transcribed onto a virtual patient simulation platform. Following completion of the module, a report was generated for each individual learner to track all responses and used as the assessment tool. The pilot test showed an increase of 28% in the pre-to-post-test results in a cohort of nine residents. The mean pre-test result of 58% increased to a mean post-test result of 86% (range: 70-100%) after completing the three modules. Conclusion VPs can be used for learning the management of oncologic emergencies and can be done on a simulation-based learning platform. The modules can be used as both, a learning and an assessment tool for junior residents. The results of the pilot test show a significant improvement in knowledge acquisition between pre- and post-test scores after completion of the three modules.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".