Poster ‐ 43: Analysis of SBRT and SRS dose verification results using the Octavius 1000SRS detector
Bibliographic record
Abstract
Purpose: The Octavius 1000SRS detector was commissioned in December 2014 and is used routinely for verification of all SRS and SBRT plans. Results of verifications were analyzed to assess trends and limitations of the device and planning methods. Methods: Plans were delivered using a True Beam STx and results were evaluated using gamma analysis (95%, 3%/3mm) and absolute dose difference (5%). Verification results were analyzed based on several plan parameters including tumour volume, degree of modulation and prescribed dose. Results: During a 12 month period, a total of 124 patient plans were verified using the Octavius detector. Thirteen plans failed the gamma criteria, while 7 plans failed based on the absolute dose difference. When binned according to degree of modulation, a significant correlation was found between MU/cGy and both mean dose difference (r=0.78, p<0.05) and gamma (r=−0.60, p<0.05). When data was binned according to tumour volume, the standard deviation of average gamma dropped from 2.2% – 3.7% for the volumes less than 30 cm3 to below 1% for volumes greater than 30 cm3. Conclusions: The majority of plans and verification failures involved tumour volumes smaller than 30 cm3. This was expected due to the nature of disease treated with SBRT and SRS techniques and did not increase rate of failure. Correlations found with MU/cGy indicate that as modulation increased, results deteriorated but not beyond the previously set thresholds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.006 | 0.001 |
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".