<sup>90</sup>Y Contamination in the Interventional Radiology Suite: VARSKIN Estimation of Skin and Eye Injury and Review of Mitigation Strategies
Bibliographic record
Abstract
Our objective was to demonstrate, through computer simulations, radiation exposure levels from a 90Y contamination event during radioembolization procedures to calculate the radiation doses from various contamination scenarios. We also provide reasonable safety protocols to prevent contamination and minimize radiation exposure during decontamination. Methods: Simulations were performed using the computer code VARSKIN+, version 1.0, to determine the amount of radiation exposure resulting from different contamination scenarios. Results: The annual radiation dose limit to the skin and the lens of the eye was exceeded within 23 s of exposure to a 44-MBq droplet. Double layers of surgical gloves and level 3 gowns provided some attenuation of radiation from 90Y contamination by reducing the dose rate by 39% and 44%, respectively. Two layers of surgical gloves offered the best ratio of radiation protection without compromising dexterity. Conclusion: This study demonstrated that radiation exposures during 90Y spills or contamination events can be considerable. Interventional radiology and nuclear medicine personnel must be mindful of the risks, follow strategies to prevent spills, and be familiar with recommended decontamination procedures for spills in the interventional radiology suite.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".