Lead-Dust Contamination on Radiation Protection Apparel
Bibliographic record
Abstract
PURPOSE: To evaluate the prevalence of surface lead-dust contamination on radiation protection apparel (RPAs) in the radiology department and compare findings with those from other studies of RPA lead-dust contamination. MATERIALS AND METHODS: A survey of RPAs was conducted between June and December 2021 in radiology departments at a tertiary-care university hospital. A convenience sample of RPAs located on wall-mounted racks outside the angiography suite and emergency department was surveyed. Surface lead dust on RPAs was detected using a rapid qualitative test. RESULTS: A total of 69 RPAs included full-length frontal lead aprons (n = 11), full-length frontal lead aprons (n = 25) with thyroid collars (n = 25), and thyroid collars alone (n = 8). Garments consisted mainly of a lead/antimony composite core with a 0.5-mm lead equivalency. One RPA failed radiologic quality inspection, and 8 garments were in poor or worn condition. The overall prevalence of surface lead-dust contamination on RPAs was 60.9% (95% CI, 49.1%-71.5%) and was significantly (P = .0035) higher on thyroid collars (78.8% [95% CI, 62.2%-89.3%]) than on lead aprons (44.4% [95% CI, 29.5%-60.4%]). CONCLUSIONS: A high prevalence of surface lead-dust contamination was detected on RPAs using a rapid qualitative test. There is currently no established safe level of lead, and these findings suggest RPAs be monitored frequently not only for physical defects limiting radiation protection but also for lead-dust contamination.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".