Sci-Thurs PM: Delivery-05: One year of learning from incidents
Bibliographic record
Abstract
The aim of this study is to quantify the effect of an incident learning system in radiation therapy. The system is designed to detect all occurrences of "an unwanted or unexpected change from a normal system behaviour that causes or has the potential to cause an adverse effect to persons or equipment". Our application to radiation therapy defines 5 incident types, four levels of severity and four work domains where errors discovered during routine quality assurance within each domain were not classified as incidents. During 2007, we recorded, corrected, investigated, determined root cause and learned from 657 incidents. The vast majority of these incidents were classified as potential minor clinical incidents having little or no impact on patient treatment. The value of the system lies in the application of the learning portion of the investigation. We demonstrated a dramatic reduction in the rate of more severe incidents by the implementation of several simple tools. Our results also show a reduction of incidents on accelerators treating essentially a single disease site. The only treatment unit treating with both image guidance and intensity modulation recorded the fewest incidents while the cobalt unit with the least technological assistance recorded three times the average treatment unit incidents with a higher severity. Additionally, although the rate of incidents at the point of treatment delivery was low, the impact of those incidents was substantially higher than that of incidents originating during treatment planning. This system has proven to be a powerful program management tool.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".