Medical imaging utilization and associated radiation exposure in children with down syndrome
Bibliographic record
Abstract
OBJECTIVE: To evaluate the frequency of medical imaging or estimated associated radiation exposure in children with Down syndrome. METHODS: This retrospective cohort study included 4,348,226 children enrolled in six U.S. integrated healthcare systems from 1996-2016, 3,095 of whom were diagnosed with Down syndrome. We calculated imaging rates per 100 person years and associated red bone marrow dose (mGy). Relative rates (RR) of imaging in children with versus without Down syndrome were estimated using overdispersed Poisson regression. RESULTS: Compared to other children, children with Down syndrome received imaging using ionizing radiation at 9.5 times (95% confidence interval[CI] = 8.2-10.9) the rate when age <1 year and 2.3 times (95% CI = 2.0-2.5) between ages 1-18 years. Imaging rates by modality in children <1 year with Down syndrome compared with other children were: computed tomography (6.6 vs. 2.0, RR = 3.1[95%CI = 1.8-5.1]), fluoroscopy (37.1 vs. 3.1, RR 11.9[95%CI 9.5-14.8]), angiography (7.6 vs. 0.2, RR = 35.8[95%CI = 20.6-62.2]), nuclear medicine (6.0 vs. 0.6, RR = 8.2[95% CI = 5.3-12.7]), radiography (419.7 vs. 36.9, RR = 11.3[95%CI = 10.0-12.9], magnetic resonance imaging(7.3 vs. 1.5, RR = 4.2[95% CI = 3.1-5.8]), and ultrasound (231.2 vs. 16.4, RR = 12.6[95% CI = 9.9-15.9]). Mean cumulative red bone marrow dose from imaging over a mean of 4.2 years was 2-fold higher in children with Down syndrome compared with other children (4.7 vs. 1.9mGy). CONCLUSIONS: Children with Down syndrome experienced more medical imaging and higher radiation exposure than other children, especially at young ages when they are more vulnerable to radiation. Clinicians should consider incorporating strategic management decisions when imaging this high-risk population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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".