Eye Lens Dosimetry in Interventional Radiology: Assessment With Dedicated Hp(3) Dosimeters
Bibliographic record
Abstract
Purpose: To quantify eye lens dose in interventional radiology and assess whether neck dosimeter is a good surrogate to evaluate eye lens dosimetry. Methods: Radiation exposure was prospectively measured in 9 interventional radiologists between May and October 2017. Standard Hp(0,07) thermoluminescent dosimeters (TLDs) were worn at the neck outside the lead apron, and 2 dedicated eye lens Hp(3) TLDs were placed just above the eyes, one midline and another at the outer edge of the left eye. Correlations between eye lens and neck TLD doses were assessed with Pearson coefficient, and linear regression was used to predict eye lens dose from neck TLD values. Results: Eye lens dose without eye protection was 0.18 ± 0.11 (mean ± standard deviation; 0.08-0.41) mSv per workday and 35.3 ± 6.6 mSv (16.3-82.9) annually (200 workdays/year). Five (56%) radiologists exceeded the 20 mSv annual eye lens dose limit. Eye lens doses from left and central TLDs were 12.46 ± 3.02 and 9.29 ± 3.38 mSv, respectively ( P = .027). Mean eye lens (left and central) and neck TLD doses were 10.87 ± 2.67 and 16.56 ± 5.67 mSv, respectively ( P = .008). Pearson correlation coefficient between both eye lens TLD and between mean eye lens TLD and neck TLD doses were 0.91 and 0.92, respectively. Average of eye lens dose was 0.0179 + (0.5971 × neck dose). Conclusion: Full-time interventional radiologists are likely to suffer from deterministic radiation effects to the eye lens, especially on the left side. Neck TLD significantly overestimates eye lens dose. However, eye lens doses are highly correlated with neck doses and may be predicted from the neck TLD values.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".