Monte Carlo-based dose reconstruction in a rat model for scattered ionizing radiation investigations
Bibliographic record
Abstract
PURPOSE: In radiation biology, rats are often irradiated, but the precise dose distributions are often lacking, particularly in areas that receive scatter radiation. We used a non-dedicated set of resources to calculate detailed dose distributions, including doses to peripheral organs well outside of the primary field, in common rat exposure settings. MATERIALS AND METHODS: We conducted a detailed dose reconstruction in a rat through an analog to the conventional human treatment planning process. The process consisted of: (i) Characterizing source properties of an X-ray irradiator system, (ii) acquiring a computed tomography (CT) scan of a rat model, and (iii) using a Monte Carlo (MC) dose calculation engine to generate the dose distribution within the rat model. We considered cranial and liver irradiation scenarios where the rest of the body was protected by a lead shield. Organs of interest were the brain, liver and gonads. The study also included paired scenarios where the dose to adjacent, shielded rats was determined as a potential control for analysis of bystander effects. RESULTS: We established the precise doses and dose distributions delivered to the peripheral organs in single and paired rats. Mean doses to non-targeted organs in irradiated rats ranged from 0.03-0.1% of the reference platform dose. Mean doses to the adjacent rat peripheral organs were consistent to within 10% those of the directly irradiated rat. CONCLUSIONS: This work provided details of dose distributions in rat models under common irradiation conditions and established an effective scenario for delivering only scattered radiation consistent with that in a directly irradiated rat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".