Computed Tomography Risk Disclosure in the Emergency Department: A Survey of Pediatric Emergency Medicine Fellowship Program Leaders
Bibliographic record
Abstract
INTRODUCTION: Given the potential malignancy risks associated with computed tomography (CT), some physicians are increasingly advocating for risk disclosure to patients/families. Our goal was to evaluate the practices and attitudes of pediatric emergency medicine (PEM) fellowship program leaders' regarding CT radiation-risk disclosure. METHODS: We conducted a cross-sectional survey study of the United States and Canadian PEM fellowship directors and associate/assistant directors. We developed a web-based survey using a modified Dillman technique. Primary outcome was the proportion who "almost always" or "most of the time" discussed potential malignancy risks from CT prior to ordering this test. RESULTS: Of 128 physicians who received the survey, 108 (86%) responded. Of those respondents, 73%, 95% confidence interval (CI) [64-81] reported "almost always" or "most of the time" discussing potential malignancy risks when ordering a CT for infants; proportions for toddlers, school-age children, and teenagers were 72% (95% CI [63-80]), 66% (95% CI [56-75]), and 58% (95% CI [48-67]), respectively (test for trend, p=0.008). Eighty percent reported being "extremely" or "very" comfortable discussing radiation risks. Factors of "high" or "very high" importance in disclosing risks included parent request for a CT not deemed clinically indicated for 94% of respondents, and parent-initiated queries about radiation risks for 79%. If risk disclosure became mandatory, 82% favored verbal discussion over written informed consent. CONCLUSION: PEM fellowship program leaders report frequently disclosing potential malignancy risks from CT, with the frequency varying inversely with patient age. Motivating factors for discussions included parental request for a CT deemed clinically unnecessary and parental inquiry about risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".