Poster — Wed Eve—17: Detrimental Dose: A Proposed Metric to Score Incidents in Radiation Therapy
Bibliographic record
Abstract
Public awareness of radiation therapy is increasing, and incidents related to radiation therapy delivery are receiving increased public scrutiny. There is a growing interest within the professional community in systems for reporting errors in radiation therapy and communicating their significance. Many of the reporting systems proposed to‐date use decision trees to stratify incidents according to differing levels of severity, with the result that it is difficult to compare incidents between disparate systems. We propose a new metric that uses absorbed dose as a basis for estimating the anticipated detriment of a radiation therapy error in much the same way as dose is used to determine risk in radiation protection. The formulation takes into account the magnitude of the dose error and the volume of tissue unintentionally irradiated, modified by dimensionless quantities that account for tissue response (Tissue Sensitivity) and patient‐specific quality of life factors (Severity Index). The resulting quantity, which we have termed “Detrimental Dose”, should be applicable to describing the severity of an adverse event regardless of the magnitude of the error. Using specific examples of recent radiation therapy incidents, we illustrate how this new metric can be used to estimate the severity of a misadministration of dose. We believe that such a system can provide a unified framework for reporting and assessment of radiation therapy errors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".