Applying epidemiologic concepts and principles to explore factors associated with error detection by radiotherapy quality assurance processes
Bibliographic record
Abstract
Background: Incident Learning System (ILS) data are used by radiotherapy (RT) quality assurance (QA) programs for the purpose of improving quality and safety of RT delivery for patients. The objectives of this thesis were: 1) to develop an analytic framework for ILS data analysis that can be used to statistically explore factors that are associated with error detection throughout reported incidents; 2) to apply the analytic framework to identify and assess factors associated with error detection by RT QA processes using a real-world ILS dataset; and 3) to apply the framework in a pre-post study to explore the effect of various changes made to RT delivery on error detection by RT QA processes. Methods: We developed an analytic framework for ILS data analysis that combined relevant incident theory with core concepts and principles of quantitative research that are common to epidemiology. The analyzes utilizing the analytic framework were conducted on an ILS dataset from the Ottawa Hospital Cancer Centre (TOHCC), Ottawa, Canada. The TOHCC ILS dataset contained 4620 characterized incidents reported at the centre from 2007-2017. Epidemiologic methods were used to identify and assess factors that were associated with error detection by specific QA processes throughout the reported incidents. Results: The analytic framework was successfully piloted on the TOHCC dataset. The RT treatment technique, treatment intent and the error domain of origin were identified as factors that were associated with successful error detection by certain RT QA processes. Changes that were made to RT delivery process at TOHCC also appeared to have both positive and negative effects on error detection by certain RT QA processes. Conclusions: We developed and piloted an analytic framework that can be used to statistically analyze ILS data to explore factors associated with error detection by RT QA processes. We have shown that with careful interpretation, this analytic approach can be used to identify factors that may contribute to error detection by redundancy implemented throughout the RT workflow sequence.
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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 | 0.051 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".