A multi-institutional CT practices survey of pediatric head, chest, and abdomen-pelvis examinations
Bibliographic record
Abstract
Background: Pediatric patients are particularly vulnerable to the stochastic effects of ionizing radiation. Despite these risks, CT remains diagnostically essential in pediatric care. Diagnostic reference levels (DRLs) have been recommended as a radiation dose optimization tool to address these concerns. Purpose: This study aims to survey pediatric CT practices at different facilities in Australia, Canada, and Norway and to suggest local DRLs (LDRLs) at each facility as a baseline for future surveys. Materials and methods: Radiation dose indices, imaging, and demographic data were collected retrospectively at each facility using PACS for unenhanced CT head, contrast-enhanced chest, and contrast-enhanced abdomen-pelvis examinations in patients from 0 to 15 years of age. The LDRL values were determined for CT dose indices and size-specific dose estimate (SSDE) values. The Kruskal–Wallis test assessed the equality of populations across countries for all dosimetric quantities. Ordinary least squares regression was employed to express SSDE as a linear function of patient weight. Results: The LDRLs for Australian, Canadian, and Norwegian facilities were determined and examined for each age group. Canadian and Norwegian LDRL data were most similar, with Australian values being comparatively lower for all categories except for 11–15-year-old abdomen-pelvis examinations. The SSDE and patient weight were significantly positively correlated for each examination/country combination. Conclusion: The proposed local reference levels can provide local baselines for dose optimization and continuous dose assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".