SU-E-T-120: Dosimetric Characteristics Study of NanoDotâ,,¢ for In-Vivo Dosimetry
Bibliographic record
Abstract
Purpose: The purpose of the study was to analyze the dosimetric characteristics (energy dependence, reproducibility and dose linearity) of nanoDot™ optically stimulated luminescence dosimeters (OSLDs) and validate their potential use during in-vivo dosimetry, specifically TBI. The manufacturer stated accuracy is ±10% for standard nanoDot™. Methods: At AKUH, the InLight microStar OSL dosimetry system for patient in-vivo dosimetry is in use since 2012. Twenty-five standard nanoDot™ were used in the analysis. Sensitivity and reproducibility was tested in the first part with 6MV and 18 MV Varian x-ray beams. Each OSLD was irradiated to 100cGy dose at nominal SSD (100 cm). All the OSLDs were read 3 times for average reading. Dose linearity and calibration were also performed with same beams in common clinical dose range of 0 – 500 cGy. In addition, verification of TBI absolute dose at extended SSD (500cm) was also performed. Results: The reproducibility observed with the OSLD was better than the manufacturer stated limits. Measured doses vary less than ±2% in 19(76%) OSLDs, whereas less than ±3% in 6(24%) OSLDs. Their sensitivity was approximately 525 counts per cGy. Better agreement was observed between measurements, with a standard deviation of 1.8%. A linear dose response was observed with OSLDs for both 6 and 18MV beams in 0 – 500 cGy dose range. TBI measured doses at 500 cm SSD were also confirmed to be within ±0.5% and ±1.3% of the ion chamber measured doses for 6 and 18MV beams respectively. Conclusion: The dosimetric results demonstrate that nanoDot™ can be potentially used for in-vivo dosimetry verification in various clinical situations, with a high degree of accuracy and precision. In addition OSLDs exhibit better dose reproducibility with standard deviation of 1.8%. There was no significant difference in their response to 6 and 18MV beams. The dose response was also linear.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".