Consensus recommendations for incident learning database structures in radiation oncology
Bibliographic record
Abstract
PURPOSE: Incident learning plays a key role in improving quality and safety in a wide range of industries and medical disciplines. However, implementing an effective incident learning system is complex, especially in radiation oncology. One current barrier is the lack of technical standards to guide users or developers. This report, the product of an initiative by the Work Group on Prevention of Errors in Radiation Oncology of the American Association of Physicists in Medicine, provides technical recommendations for the content and structure of incident learning databases in radiation oncology. METHODS: A panel of experts was assembled and tasked with developing consensus recommendations in five key areas: definitions, process maps, severity scales, causality taxonomy, and data elements. Experts included representatives from all major North American radiation oncology organizations as well as users and developers of public and in-house reporting systems with over two decades of collective experience. Recommendations were developed that take into account existing incident learning systems as well as the requirements of outside agencies. RESULTS: Consensus recommendations are provided for the five major topic areas. In the process mapping task, 91 common steps were identified for external beam radiation therapy and 88 in brachytherapy. A novel feature of the process maps is the identification of "safety barriers," also known as critical control points, which are any process steps whose primary function is to prevent errors or mistakes from occurring or propagating through the radiotherapy workflow. Other recommendations include a ten-level medical severity scale designed to reflect the observed or estimated harm to a patient, a radiation oncology-specific root causes table to facilitate and regularize root-cause analyses, and recommendations for data elements and structures to aid in development of electronic databases. Also presented is a list of key functional requirements of any reporting system. CONCLUSIONS: Incident learning is recognized as an invaluable tool for improving the quality and safety of treatments. The consensus recommendations in this report are intended to facilitate the implementation of such systems within individual clinics as well as on broader national and international scales.
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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 | 0.274 | 0.415 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.017 | 0.013 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".