In plain language
Using existing physician dosimetry data, researchers derived an equation to estimate effective dose from a single dosimeter worn over non-lead aprons. It slightly overestimated dose at most, and a thyroid collar lowered estimated dose by 17%-30%.
At a glance
- Type of study
- Original research, Modeling or economic study
- Who was studied
- Other physicians
- Size
- n = 205 dosimeter readings
- Topics
- Dosimetry and monitoringPersonal protective equipmentMusculoskeletal injury and ergonomicsThyroidRegulation and policy
- Summary based on
- the abstract
What it found
A new equation using nominal lead-equivalent thickness estimates effective dose from a single outside-apron dosimeter for non-lead aprons, with minimal underestimation and up to 1.3-fold overestimation.
Key figures: E=H0(0.13+1.02e(-11.33x)); up to 1.3-fold overestimation
The method improved on previous single-dosimeter methods.
Key figures: 15%
Adding a non-lead thyroid collar reduced effective dose.
Key figures: 17%-30%
The method requires use of accessory upper body shielding.
The dataset came from previously published dosimetry on physicians.
Key figures: 205 dosimeter readings, 42 physicians, 8243 procedures
Limits to keep in mind
Secondary modeling analysis of one previously published dataset, validated against two-dosimeter algorithms rather than true measured effective dose; applicability depends on accessory upper body shielding being used.
Related research summaries
- Radiation exposure in interventional cardiology: a narrative review of health effects and novel approaches to ensure radiation safety (2025, Narrative review)
- Radiation Protection in Interventional Radiology (2022, Narrative review)
- AST Standards for Ionizing Radiation Exposure (Guideline or consensus statement)
- Occupational Radiation Exposure and Thyroid Nodules in Healthcare Workers: A Review (2025, Narrative review)
- Radiation Exposure, Training, and Safety in Cardiology (2024, Narrative review)
How this summary was made
ORSIF prepared this summary with AI assistance from the abstract of the paper. A program then checked that the passage supporting each finding appears word for word in that source. A person has not yet reviewed each summary, so read the original before relying on a number. ORSIF summarizes research and does not endorse any product or company. Report a problem with this summary.
Prepared November 3, 2026.