Poster — Thur Eve — 56: A Comprehensive System for Classifying, Recording and Analyzing Incidents, Accidents, and Other Events in a Radiotherapy Practice
Bibliographic record
Abstract
Introduction: An event reporting program was created in a large radiotherapy facility. The aim of the program was develop a classification system for radiation events to improve quality in patient treatment. An event was defined as any action which caused or could have caused the incorrect treatment of the patient or led to the delay of a patient's treatment. Methods and Materials: An event reporting committee comprising of senior members of staff was formed and an anonymous event reporting form and mechanism were created. The data was analyzed and educational feedback were given to the entire staff. Results: The project is ongoing at our clinic, but the data was collected over a 3‐year period starting January 1, 2007. Events are initially evaluated immediately after occurrence, and every few weeks the committee assembles and categorizes the events reports. A total of 971 events were recorded. After an initial increase from 335 events in 2007, to 437 in 2008, 2009 registered only 199 errors. The total event rate per fraction is less than 1% (0.00719), but the overall event rate per patient is about 11%. Also we note that the accident rate (events which have dosimetric consequences for the patient) is relatively low with only 39 events for the 3 year period, about 1 event per month. We also note that near misses account for about 15% of all events. The largest number events (∼15%) occur during treatment delivery, this is expected as this is the most frequent activity.
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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 | 0.023 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.036 |
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".