A retrospective analysis of paediatric medical imaging utilisation in Ireland, 2019-2023
Bibliographic record
Abstract
INTRODUCTION: While longitudinal studies in the United States and Canada show increased use of non-ionising imaging and reduced paediatric CT, recent data on imaging trends in Europe are lacking. This study examines paediatric imaging trends from 2019 to 2023 in Ireland, including age-specific patterns and procedure frequencies. METHODS: A retrospective cohort analysis was conducted within Irish paediatric hospitals. Data extracted from the Radiology Information Systems, including examination counts for conventional X-ray (XR), computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), nuclear medicine (NM), and fluoroscopically guided procedures in patients under 18 years. Examinations were categorised by modality, anatomical region, and age group. Annual imaging volumes and utilisation trends were assessed. RESULTS: A total of 634,770 examinations were performed: 73.7% XR, 12.4% US, 5.4% MRI, 4.4% CT, 3.4% fluoroscopy, and 0.63% NM. Ionising radiation was used in 82.2% of all examinations. Imaging volumes declined by 15.2% in 2020 due to the COVID-19 pandemic but recovered fully by 2022. From 2019 to 2023, CT (+20.3%), US (+16.5%), and MRI (+8.6%) showed the highest growth. XR and ultrasound were most common in infants, while CT and MRI showed a more uniform distribution, with brain imaging being the most frequent examination for both. CONCLUSIONS: This contemporary multi-centre analysis of paediatric imaging in Ireland demonstrates a sustained reliance on XR and US, increasing CT utilisation, underscoring the ongoing need for justification and radiation dose optimisation in paediatric practice. It also highlights the influence of the COVID-19 pandemic on imaging trends.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.000 | 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".