Childhood exposure to ionizing radiation from computed tomography imaging in Nova Scotia
Bibliographic record
Abstract
BACKGROUND: Examining radiation dose in the paediatric population is particularly important due to the vulnerability of paediatric patients (increased radiosensitive tissues and postexposure life-years) and risk for future radiogenic malignancy. OBJECTIVES: To evaluate trends in paediatric computed tomography (CT) use and ionizing radiation exposure using population-based data from Nova Scotia. METHODS: A retrospective, population-based cohort study of CT use in patients <20 years of age, from January 1, 2004 to December 31, 2011, was performed in Nova Scotia. CT examination data were retrieved from a provincial imaging repository. Trends in CT use were described, and both annual and cumulative effective dose exposures were calculated. RESULTS: In total, 29,452 CT events, involving up to 22,867 individuals were retrieved. Overall annual paediatric CT examination rates remained static (range 17.4 to 18.8 per 1000 per year). However, use in children <10 years of age decreased by >50% (P<0.001); this was counterbalanced by a steady increase among 15- to 19-year-olds (P<0.0001). Overall, 15.4% of scanned patients underwent ≥2 examinations, of which 58 patients (1.6%) exceeded 50 mSv of exposure. CONCLUSIONS: Despite a static rate in CT imaging among the entire cohort, children <15 years of age and, particularly, those <10 years of age displayed marked reductions in CT use. This may reflect increased awareness of campaigns emphasizing judicious CT use, revised clinical practice guidelines and increased availability of alternative modalities. A small subgroup demonstrated high-dose exposure (>50 mSv), and rates in individuals >15 years of age steadily increased, suggesting further exposure reduction efforts are necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".