MO‐E‐17A‐09: Has Cancer Risk for Pediatric CT Increased Or Decreased? An Analysis of Cohort Data From 2004‐2013
Bibliographic record
Abstract
Purpose: To analyze CT radiation dosimetry trends in a pediatric population imaged with modern (2004‐2013) CT technology Methods: The institutional review board approved this retrospective review. Two cohorts of pediatric patients that received CT scans for treatment or surveillance for Wilms tumor (n=73) or Neuroblastoma (n=74) from 2004–2013 were included in this study. Patients were scanned during this time period on a GE Ultra (8 slice; 2004–2007), a GE VCT (2008–2011), or a GE VCT‐XTe (2011–2013). Each patient's individual or combined chest, abdomen, and pelvic CT exams (n=4138) were loaded onto a PACS workstation (Intelerad, Canada) and measured to calculate their effective diameter and SSDE. Patient SSDE was used to estimate patient organ dosimetry based on previously published data. Patient's organ dosimetry were sorted by gender, weight, age, scan protocol (i.e., chest, abdomen, or pelvis), and CT scanner technology and averaged accordingly to calculate population averaged absolute and effective dose values. Results: Patient radiation dose burden calculated for all genders, weights, and ages decreased at a rate of 0.2 mSv/year (4.2 mGy/year; average organ dose) from 2004–2013; overall levels decreased by 50% from 3.0 mSv (60.0 mGy) to 1.5 mSv (25.9 mGy). Patient dose decreased at equal rates for both male and female, and for individual scan protocols. The greatest dose savings was found for patients between 0–4 years old (65%) followed by 5‐9 years old (45%), 10–14 years old (30%), and > 14 years old (21%). Conclusion: Assuming a linear‐nothreshold model, there always will be potential risk of cancer induction from CT. However, as demonstrated among these patient populations, effective and organ dose has decreased over the last decade; thus, potential risk of long‐term side effects from pediatric CT examinations has also been reduced.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".