Estimating Effective Dose to Pediatric Patients Undergoing Interventional Radiology Procedures Using Anthropomorphic Phantoms and MOSFET Dosimeters
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to estimate the effective doses received by pediatric patients during interventional radiology procedures and to present those doses in "look-up tables" standardized according to minute of fluoroscopy and frame of digital subtraction angiography (DSA). MATERIALS AND METHODS: Organ doses were measured with metal oxide semiconductor field effect transistor (MOSFET) dosimeters inserted within three anthropomorphic phantoms, representing children at ages 1, 5, and 10 years, at locations corresponding to radiosensitive organs. The phantoms were exposed to mock interventional radiology procedures of the head, chest, and abdomen using posteroanterior and lateral geometries, varying magnification, and fluoroscopy or DSA exposures. Effective doses were calculated from organ doses recorded by the MOSFET dosimeters and are presented in look-up tables according to the different age groups. RESULTS: The largest effective dose burden for fluoroscopy was recorded for posteroanterior and lateral abdominal procedures (0.2-1.1 mSv/min of fluoroscopy), whereas procedures of the head resulted in the lowest effective doses (0.02-0.08 mSv/min of fluoroscopy). DSA exposures of the abdomen imparted higher doses (0.02-0.07 mSv/DSA frame) than did those involving the head and chest. CONCLUSION: Patient doses during interventional procedures vary significantly depending on the type of procedure. User-friendly look-up tables may provide a helpful tool for health care providers in estimating effective doses for an individual procedure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".