Isopropanol-based polymer gel dosimeters for use with x-ray CT imaging
Bibliographic record
Abstract
We report on investigations aimed at increasing the dose response sensitivity and resolution in x-ray CT imaging of polymer gel dosimeters (PGD). We incorporate isopropanol as co-solvent into the gel formulation and show that this incorporation increases dose sensitivity and dose resolution of x-ray CT imaged gel dosimeters. These gels are reproducible in response and stable post-irradiation. We apply the system to a simple 1L gel test case where 2 separate irradiations are used to generate a dose response calibration curve. A third irradiation (3-field) is then calibrated and compared to treatment planning predictions. Results indicate that isopropanol-based PGDs are promising formulations for x-ray CT gel dosimetry and that this current system outperforms previous attempts at dose reconstruction using x-ray CT imaging of PGDs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".