Investigation into scatter radiation dose levels received by a restrainer in small animal radiography
Bibliographic record
Abstract
OBJECTIVES: To measure the intensity and distribution of scatter radiation received by a restrainer in veterinary radiography including the intensity of scatter radiation passing through lead protective devices at pre-defined positions. METHODS: Anthropomorphic phantoms and a Labrador dog cadaver were used to simulate a restrainer and patient. Scatter dose measurements were recorded at the position of the restraining hands, thyroid, breast and gonads with and without appropriate lead protection. This was repeated for the eight most common projections as identified in an initial retrospective survey. RESULTS: Manual restraint of an animal for a radiographic procedure will result in a scatter radiation dose to the restrainer. The level of radiation dose varies between body regions and between projections. The use of appropriate lead protection resulted in statistically significant dose reductions to all body regions with maximum scatter dose reductions between 93 and 100%. CLINICAL SIGNIFICANCE: While the doses recorded were small (μGy) in terms of associated risk, they are nonetheless cumulative which can result in a more significant dose. Therefore manual restraint should be avoided and forms of immobilisation should be used such as mechanical means, sedation or general anaesthesia. However, if completely necessary both principles of distance and adequate lead protection should be employed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".