OPTIMIZING PEER REVIEW ROUNDS IN RADIATION ONCOLOGY: A SCOPING REVIEW
Bibliographic record
Abstract
In Radiation Oncology (RO), peer review (PR) rounds are essential for ensuring quality care, enhancing team communication, and identifying areas for improvement in radiotherapy plans. However, time constraints, lengthy discussions, and imbalanced team contributions often hinder effective PR. This scoping review examined novel tools and processes to enhance PR efficiency and experience in modern academic centres. We queried seven databases (MEDLINE (Ovid), EMBASE, PubMed, Cochrane Library, CINAHL, and MEDLINE (Ebsco), along with the grey literature, yielding 4,621 citations. Studies were excluded if they were (1) evaluated against paper-based rounds or lacked clear relevance to (2) RO PR processes, or (3) efficiency for RO rounds. Twelve studies focusing on PR structure and efficiency-related processes were included. Of the identified, 11/12 explored various structural formats to improve facilitation, 4/12 discussed automated tools and 2/12 had checklists. Only half of studies reported a PR-associated time burden, with 2/12 reporting positive post-implementation changes. The remaining studies did not measure comparative times. Our review revealed a significant gap in research aimed at improving PR efficiency in RO, despite lack of efficiency and resulting high time commitment being a commonly reported participation barrier. These findings emphasize the potential benefits of incorporating automation, improving the ways we facilitate rounds, and utilizing tools such as checklists, given PR’s critical role in patient safety and ongoing clinical learning. This scoping review reveals the lack of work on innovative approaches to optimize PR rounds in RO and underscores the need to enhance both effectiveness and efficiency. Future research should focus on developing and evaluating time-saving strategies and tools for PR in radiation oncology settings, with particular attention to the unique challenges and opportunities within the Canadian healthcare system.
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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 | 0.203 | 0.439 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.035 | 0.038 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".