Radiation Exposure of the Anesthesiologist in the Neurointerventional Suite
Bibliographic record
Abstract
BACKGROUND: Scatter radiation during interventional radiology procedures can produce cataracts in participating medical personnel. Standard safety equipment for the radiologist includes eye protection. The typical configuration of fluoroscopy equipment directs radiation scatter away from the radiologist and toward the anesthesiologist. This study analyzed facial radiation exposure of the anesthesiologist during interventional neuroradiology procedures. METHODS: Radiation exposure to the forehead of the anesthesiologist and radiologist was measured during 31 adult neuroradiologic procedures involving the head or neck. Variables hypothesized to affect anesthesiologist exposure were recorded for each procedure. These included total radiation emitted by fluoroscopic equipment, radiologist exposure, number of pharmacologic interventions performed by the anesthesiologist, and other variables. RESULTS: Radiation exposure to the anesthesiologist's face averaged 6.5 ± 5.4 μSv per interventional procedure. This exposure was more than 6-fold greater (P < 0.0005) than for noninterventional angiographic procedures (1.0 ± 1.0) and averaged more than 3-fold the exposure of the radiologist (ratio, 3.2; 95% CI, 1.8-4.5). Multiple linear regression analysis showed that the exposure of the anesthesiologist was correlated with the number of pharmacologic interventions performed by the anesthesiologist and the total exposure of the radiologist. CONCLUSIONS: Current guidelines for occupational radiation exposure to the eye are undergoing review and are likely to be lowered below the current 100-150 mSv/yr limit. Anesthesiologists who spend significant time in neurointerventional radiology suites may have ocular radiation exposure approaching that of a radiologist. To ensure parity with safety standards adopted by radiologists, these anesthesiologists should wear protective eyewear.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".