Remote Contouring and Virtual Review during the COVID-19 Pandemic (RECOVR-COVID19): Results of a Quality Improvement Initiative for Virtual Resident Training in Radiation Oncology
Bibliographic record
Abstract
The need to minimize in-person interactions during the COVID-19 pandemic has led to fewer clinical learning opportunities for trainees. With ongoing utilization of virtual platforms for resident education, efforts to maximize their value are essential. Herein we describe a resident-led quality improvement initiative to optimize remote contouring and virtual contour review. From April to June 2020, radiation oncology (RO) residents at our institution were assigned modified duties. We implemented a program to source and assign cases to residents for remote contouring and to promote and optimize virtual contour review. Resident-perceived educational value was prospectively collected and analyzed. All nine RO residents at our institution (PGY1–5) participated, and 97 cases were contoured during the evaluation period. Introduction of the Remote Contouring and Virtual Review (RECOVR) program coincided with a significant increase in mean cases contoured per week, from 5.5 to 17.3 (p = 0.015), and an increased proportion of cases receiving virtual review, from 14.8% to 58.6% (p < 0.001). Residents reported that the value of immediate feedback during virtual review was similar to that of in-person review (4.6 ± 0.1 vs. 4.5 ± 0.2, p = 0.803) and significantly higher than feedback received post hoc (e.g., email; 3.6 ± 0.2, p < 0.001). The implementation of a remote process for contour review led to significant increases in contouring, and virtual contour review was rated as highly as in-person interactions. Our findings provide a data-driven rationale and framework for integrating remote contouring and virtual review into competency-based medical education.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".