Electron, Photon, and Neutron Dose Conversion Coefficients of Lens and Non-Lens Tissues Using a Multi-Tissue Eye Model to Assess Risk of Cataracts and Retinitis
Bibliographic record
Abstract
Previous publications describe the estimation of the dose from ionizing radiation to the whole lens or parts of it but have not considered other eye tissues that are implicated in cataract development; this is especially critical for low-dose, low-ionizing-density exposures. A recent review of the biological mechanisms of radiation-induced cataracts showed that lenticular oxidative stress can be increased by inflammation and vascular damage to non-lens tissues in the eye. Also, the radiation oxygen effect indicates different radiosensitivities for the vascular retina and the severely hypoxic lens. Therefore, this study uses the Monte Carlo N-Particle simulations to quantify dose conversion coefficients for several eye tissues for incident antero-posterior exposure to electrons, photons, and neutrons (and the tertiary electron component of neutron exposure). A stylized, multi-tissue eye model was developed by modifying a model by Behrens etal. (2009) to include the retina, uvea, sclera, and lens epithelial cell populations. Electron exposures were simulated as a single eye, whereas photon and neutron exposures were simulated employing two eyes embedded in the ADAM-EVA phantom. For electrons and photons, dose conversion coefficients are highest for either anterior tissues for low-energy incident particles or posterior tissues for high-energy incident particles. Neutron dose conversion coefficients generally increase with increasing incident energy for all tissues. The ratio of the absorbed dose delivered to each tissue to the absorbed dose delivered to the whole lens demonstrated the considerable deviation of non-lens tissue doses from lens doses, depending on particle type and its energy. These simulations demonstrate that there are large variations in the dose to various ocular tissues depending on the incident radiation dose coefficients; this large variation will potentially impact cataract development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".