Modelling the dynamic dose response of an nMAG polymer gel dosimeter
Bibliographic record
Abstract
Gel dosimetry measures the absorbed radiation dose with high spatial resolution in 3D. However, recently published data show that the response of metacrylic-based polymer gels depends on the segmented delivery pattern, which could potentially be a considerable disadvantage for measurements of modern dynamic radiotherapy techniques. The aim of this study is to design a dynamic compartment model for the response of a gel dosimeter, exposed to an arbitrary irradiation pattern (segmented delivery and intensity modulation), in order to evaluate the associated effects on absorbed dose measurements. The model is based on the separation of the protons affecting the magnetic resonance signal (i.e. the R2 value) into six compartments, described by a set of differential equations. The model is used to calculate R2 values for a number of different segmented delivery patterns between 0-4 Gy over 1-33 fractions. Very good agreement is found between calculated and measured R2 values, with an average difference of 0.3 ± 1.1% (1 SD). The model is also used to predict the behaviour of a gel dosimeter exposed to irradiation according to typical IMRT, VMAT and respiratory gating scenarios. The calculated R2 values are approximately independent of the segmented delivery, given that the same total dose is delivered during the same total time. It is concluded that this study helps to improve the theoretical understanding of the dependence of metacrylic-based polymer gel response to segmented radiation delivery.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".