Quantifying and Contextualizing Radiation Doses in Common Pediatric Medical Imaging Examinations
Bibliographic record
Abstract
Objective: To provide organ and effective radiation doses for common pediatric imaging examinations, which may help clinicians understand site-specific cancer risk, compare exposure across imaging modalities, and make informed care decisions. Study design: Within a large multicenter retrospective cohort, imaging utilization and associated radiation doses were estimated for children enrolled from birth into one of six US health care systems. Doses are described for examinations performed from 2012 to 2017. For computed tomography (CT), doses were estimated using examination-level technical parameters, patient height and weight, and Monte Carlo simulations. For fluoroscopy, angiography, nuclear medicine, and radiography, dose maps were developed by patient age, sex, size, and year through Monte Carlo simulations using technical parameters collected from patient examinations. The mean dose and standard deviation (SD) were calculated for each examination type, and each modality's contribution to the cohort's cumulative effective dose was calculated. Results: Eight hundred thirty-five thousand six hundred forty-three imaging examinations in 278 909 patients are included. Radiographs were the most commonly performed exam but made up 6% of radiation dose exposure. CT exams made up 4% of imaging exams but accounted for 80% of exposure. Head CT was the most common CT exam (44% of all CT). For head CT, the average radiation dose to the bone marrow (associated with hematologic cancer risk) was 9.8 mGy (SD = 6.7) and to the brain (associated with brain cancer risk) was 39 mGy (SD = 14.8). Conclusions: CT radiation doses to the bone marrow and brain fell within ranges associated with increased hematologic and brain cancer risk, and are highest in the youngest children.Keywords: Organ dose; effective dose; pediatric imaging; pediatric radiology; computed tomography; fluoroscopy; angiography; nuclear medicine; radiography; x-ray.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".