Poster — Thurs Eve‐42: A revision of the γ‐evaluation: Initial interpretation of dose disagreements on γ‐vector fields
Bibliographic record
Abstract
Gamma evaluations are a common clinical tool used as a quantitative comparison between dose-distributions, combining both dose difference and distance to agreement criteria. Because gamma evaluations permit rapid analysis of agreement between complex dose distributions, they are often a preferred comparison method for assessing delivery of conformal radiotherapy distributions. Although the comparison provides a useful measure of agreement between distributions when the index is less than one, the scalar gamma value provides little information into the clinical significance or source of disagreements of failing gamma values (i.e., when γ>1). Previously, Stock et al., have presented the gamma angle as an indicator of the relative influence of the distance to agreement versus the dose difference on gamma. We present a modification to the gamma evaluation such that the complete 3D gamma vector information is considered. The predictive nature of each vector component was investigated by simulating various dose disagreements in test distributions. Misalignment tests revealed that the mean gamma vector components indicate the offset direction and relative magnitude for all test distributions. The mean dose component of the gamma vector was prognostic of double Gaussian overdoses and underdoses in a virtual conformal delivery. The response of the vector field depends on properties distinctive to each distribution, such as the local dose gradient. Understanding how these unique properties affect the vector field may permit better diagnosis of dose disagreement sources. Other vector field properties, such as curl and divergence, may yet provide more information for interpreting the cause and significance of γ>1.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".