Pre‐clinical and clinical evaluation of the HYPERSCINT plastic scintillation dosimetry research platform for in vivo dosimetry during radiotherapy
Bibliographic record
Abstract
PURPOSE: The purpose of this work is to evaluate the Hyperscint-RP100 scintillation dosimetry research platform (Hyperscint-RP100, Medscint Inc., Quebec, QC, Canada) designed for clinical quality assurance (QA) for use in in vivo dosimetry measurements. METHODS: The pre-clinical evaluation of the scintillator was performed using a Varian TrueBeam linear accelerator. Dependency on field size, depth, dose, dose rate, and temperature were evaluated in a water tank and compared to calibration data from commissioning and annual QA. Angularity was evaluated with a 3D printed phantom. The clinical evaluation was first performed in two cadaver dogs, and then in three companion animal dogs receiving radiation therapy for nasal tumors. A treatment planning CT scan was performed for cadavers and clinical patients. Prior to treatment, the probe was inserted into the radiation field. Radiation was then delivered and measured with the scintillator. For cadavers, the treatment was repeated after making an intentional shift in patient position to simulate a treatment error. RESULTS: In the preclinical measurements the dose differed from annual measurements as follows: field size -0.77 to 0.43%, depth dose -0.36 to 1.14%, dose -0.54 to 2.93%, dose rate 0.3 to 3.6%, and angularity -1.18 to 0.01%. Temperature dependency required a correction factor of 0.11%/°C. In the two cadavers, the dose differed by -1.17 to 0.91%. The device correctly detected the treatment error when the heads were intentionally laterally shifted. In three canine clinical patients treated in multiple fractions, the detected dose ranged from 98.33 to 103.15%. CONCLUSION: Results of this new device are promising although more work is necessary to fully validate it for clinical dosimetry.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".