Workers should take steps to mitigate surface lead exposure when using lead‐containing personal protective equipment
Bibliographic record
Abstract
Abstract Veterinary workers use lead shielding for protection against harm from ionizing radiation during diagnostic imaging. Surface lead on shielding has been reported as a potential exposure hazard. The purpose of this prospective, analytical, descriptive study was to measure surface lead on lead‐containing shielding in the field service and small animal radiology areas of a veterinary teaching hospital, and to examine potential risk factors for surface lead. Surface lead on a convenience sample of 54 lead shielding items was measured in a commercial laboratory using inductively coupled plasma mass spectrometry. The median and range of surface lead for aprons, thyroid collars, and gloves were 3.6 μg/dm2 (range, 1.3‐22.4 μg/dm2), 23.1 μg/dm2 (range, 2.6‐116 μg/dm2), and 134 μg/dm2 (range, 1.5‐155 μg/dm2), respectively. In the final multivariable analysis, the mean surface lead on thyroid collars and gloves was higher than on aprons (relative differences 4.8, 95% confidence interval [CI] 2.2, 10.5, P < 0.001; and 9.5, 95% CI 4.4, 20.6, P < 0.001, respectively). The mean surface lead on shielding in the worst condition was higher than on shielding in the intermediate and best conditions (P < 0.001). Lead shielding provides effective protection against the harmful effects of ionizing radiation, and shielding should always be worn during radiation exposure. Based on our findings and the common use of lead shielding in veterinary workplaces, we recommend that employers inform workers of this hazard and provide training on steps to mitigate exposure, including the use of disposable gloves when wearing lead shielding and handwashing after use.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".