Poster ‐ 27: Incident Learning Practices in Ontario
Bibliographic record
Abstract
Purpose: The Radiation Incident and Safety Committee (RISC), established and supported by Cancer Care Ontario (CCO), is responsible for advising the Provincial Head of the Radiation Treatment program on matters relating to provincial reporting of radiation incidents with the goal of improved risk mitigation. Methods: The committee is made up of Radiation Incident Leads (RILs) with representation from each of the 14 radiation medicine programs in the province. RISC routinely meets to review recent critical incidents and to discuss provincial reporting processes and future directions of the committee. Regular face to face meetings have provided an excellent venue for sharing incident learning practices. A summary of the incident learning practices across Ontario has been compiled. Results: Almost all programs in Ontario employ an incident learning committee to review incidents and identify corrective actions or process improvements. Tools used for incident reporting include: paper based reporting, a number of different commercial products and software solutions developed in‐house. A wide range of classification schema (data taxonomies) are employed, although most have been influenced by national guidance documents. The majority of clinics perform root cause analyses but utilized methodologies vary significantly. Conclusions: Most programs in Ontario employ a committee approach to incident learning. However, the reporting tools and taxonomies in use vary greatly which represents a significant challenge to provincial reporting. RISC is preparing to adopt the National System for Incident Reporting – Radiation Therapy (NSIR‐RT) which will standardize incident reporting and facilitate data analyses aimed at identifying targeted improvement initiatives.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".