Development of<sup>P</sup>DRESS (Patient Specific Dose Real Evaluation Systems) using a TENOMAG Gel and Optical CT (VISTA<sup>TM</sup>) in Clinical IMRT Prostate Case
Bibliographic record
Abstract
The aims of this study, we present the preliminary results of 3 dimensional dose evaluation software ( P DRESS, patient specific dose real evaluation systems). In this work, we compared planned 3D dose distribution with measured 3D dose distribution using a novel normoxic polymer gel dosimeter (TENOMAG) and a commercial cone-beam optical CT scanner (VISTA TM , Modus Medical Devices, Inc., London, ON, Canada) to verify the 3D dose distribution in intensity-modulated radiation therapy (IMRT) prostate case. And we developed P DRESS using the Xelis Flatform which is developed by INFINITT Corporation is used to display the 3D dose distribution by loading the DICOM RT Data which is exported from RTP and optical-CT reconstructed VFF file. Data analysis is achieved by comparing the RTP data with the VFF data using profile, gamma map, and DTA. The profiles showed good agreement between RTP data, gel dosimeter, and gamma distribution and the precision of the dose distribution is within ± 5%. The results from this study show that there are no significantly discrepancies between the calculated dose distribution from treatment plan and the measured dose distribution from a TENOMAG gel scanned with an optical CT scanner. The 3D dose evaluation software ( P DRESS) which is developed in this study evaluates the accuracy of the three dimensional dose distributions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".