Poster - Thur Eve - 17: Control point analysis comparison of three different treatment planning and delivery complexity levels using a commercial three dimensional diode array
Bibliographic record
Abstract
PURPOSE: To use Control Point Analysis (Sun Nuclear Corporation, Melbourne, Florida, USA) to analyze and compare delivered VMAT plans for three different treatment planning complexity levels. METHODS: and delivered on a Varian LINAC. The delivered dose was measured using ArcCHECK™. Each plan was analyzed using SNC Patient 6 and Control Point Analysis. Gamma passing percentage was used to assess the differences between the measured and planned dose distributions and to assess the role of various control point binning scenarios. RESULTS: The prostate cases reported the highest gamma passing percentages for SNC Patient 6 (99.3%-99.5%,3%/3mm) and Control Point Analysis (99.1--99.3%,3%/3mm). The mean percentage of passing control point sectors for the prostate cases increased from 48.9±3.1% for individual control points to 69.5 ± 3.9% for 5 control points binned together to 100±0% for 10 control points binned together. Over all, there was a trend in the percentage of sectors passing gamma analysis increasing with the increase of the number of control points binned together in one sector for both passing criteria considered (48.9±3.1% for individual control points to 69.5±3.9% for 5 control points binned together in one sector to 100±0% for 10 control points binned together in one sector for the prostate). CONCLUSION: The delivery accuracy per control point depends on the MU/control point (SBRT) and the plan degree of modulation (H&N).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".