Radiation Safety Education and Practices in Urology: A Review
Bibliographic record
Abstract
Introduction:Radiation safety education is important as fluoroscopy is commonly used for diagnostic and therapeutic purposes. Exposure to high levels of ionizing radiation is associated with an increased risk of cancer and other adverse health effects; therefore, it is essential that urologists and trainees are educated on the safe use of radiation. Unfortunately, radiation education and occupational safety is not standardized for this group and there are currently no review studies examining radiation safety for urologists in the clinical setting. This review aims at investigating the various levels of radiation safety education and practices used in urology. Methods:MEDLINE and EMBASE databases were searched for relevant publications reporting on radiation knowledge and randomized controlled trials, non-randomized comparative studies, and observational studies were included. Reviews, abstracts, editorial comments, non-urologic studies, and incomplete articles were excluded. Results:Within these articles, there were 16 observational studies. Frequency of radiation exposure ranged from <1 × to >15 × /week. There were higher rates of adherence to use of lead aprons and thyroid shields than lead eyeglasses and gloves. Radiation safety education was infrequent. Radiation safety knowledge was especially low for the risks of radiation exposure. Most studies highlight the need for increased awareness and training on radiation safety for both urology trainees and consultants. Conclusions:Radiation safety education and practices are an important issue in urology. Improvements to education and compliance to radiation safety practices are critical to ensuring urologists and trainees use ionizing radiation in a safe and responsible manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".