A survey of local diagnostic reference levels for the head, thorax, abdomen and pelvis computed tomography in Norway and Canada
Bibliographic record
Abstract
Background Computed tomography (CT) contributes to 60% of the collective dose in medical imaging. Literature has demonstrated that patient dose varies across regions and countries. Establishing diagnostic reference levels (DRLs) contributes to the optimization of clinical practices and radiation protection. Purpose To survey the dose indices (CTDIvol and dose-length product) for frequently performed CT examinations from the chosen hospitals in Norway and Canada and to determine local DRLs (LDRLs) based on the collected data. Material and Methods The survey included eight scanners from two Norwegian hospitals and four scanners from four Canadian hospitals. Dosimetry data were collected for the following routine CT examinations: head, contrast-enhanced thorax, and abdomen and pelvis. Overall 480 adult average-sized patients from Norway and 360 from Canada were included in the survey. The LDRLs were determined as the 75th percentile of distributions of median values of dose indicators from different CT scanners. The differences in dose between scanners were determined using single-factor ANOVA. Results The LDRLs determined in Norway were higher overall than in Canada. The obtained values were compared to the national DRLs. The dose from several scanners in Norway exceeded national Norwegian DRLs, while Canadian LDRLs were below the Canadian reference levels. The differences between the means of the dose distributions from each scanner were statistically significant ( p < 0.05) for all examinations with exception of identical scanners located in the same hospital and using the same protocols. Conclusion Observed dose variations even in the same hospital, or from the same scanner model confirmed the need for CT protocol optimization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".