16 The Use of MRI-Based Contour in Assessing the Impact of Hydrogel Spacer on Rectal Dosimetry in Prostate Stereotactic Radiotherapy
Bibliographic record
Abstract
departments.Each department has a Radiation Treatment Quality Assurance Committee (RTQAC), reporting to the Provincial RTQAC, and subsequently into the provincial quality framework.Here we report on 10 months experience utilizing the NSIR-RT taxonomy within a provincial quality framework. Materials and Methods:The 2017 NSIR-RT Minimal Data Set was populated as a stand-alone form within the RL Solutions incident reporting platform.Statistics were generated through the RL Solutions analytics tools based on: acute medical severity, dosimetric harm, problem type, and volume stratified by "technique" and "body regions treated".Following multidisciplinary review by the departmental incident reporting and learning team, incident analyses were completed, recommendations were captured using a provincial action items template, and shared learning was provided through case study presentations, workshops and new policy teaching sessions.Results: Excluding the category of "other," the top three problem types were: "radiation therapy scheduling," "patient position, setup point or shift" and "treatment accessories" related incidents.Acute medical severity coding triggered concise incident analyses which included: timelines, contributing factors and provincial and/or multidisciplinary recommendations.Combining volume trends based on "technique" and "body region treated" allowed for the rapid identification of incidents attributed to a change in treatment indications, resulting in a cascade of differing problem types, which led to policy and process revision and standardization.Four provincial and/or multidisciplinary shared learning events were created in response to incident reporting and learning.Conclusions: We established a provincially integrated system for incident reporting and shared learning across departments.In the first 10 months of incident reporting using the NSIR-RT taxonomy, we have been able to successfully identify areas of interrelated process change, policy revision and standardization, requiring collaborative departmental action. 16
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".