Radiation Dose Survey for Common Computed Tomography Exams: 2013 British Columbia Results
Bibliographic record
Abstract
In 2013 Health Canada conducted a national survey of computed tomography (CT) radiation usage. We analysed contributions from all 7 public health authorities in the province of British Columbia, which covered scanner age, number of slices, and common adult protocols (≥ 19 years: 70 ± 20 kg, head, chest, abdomen/pelvis, and trunk). Patient doses were recorded for common protocols. Diagnostic reference levels (DRLs) was calculated using scanner data with >10 patient doses recorded for each protocol. Data was analysed based on image reconstruction (filtered backprojection vs iterative reconstruction [IR] vs IR available but not in use). Provincial response was 92%, with 59 of 64 CT data used for analysis. The average scanner age was 5.5 years old, with 39% of scanners installed between 2008-2013; 78.5% of scanners were multislice (>64 slices), and 44% of scanners had IR available. Overall British Columbia DRLs were: head = 1305, chest = 529, abdomen/pelvis = 819, and trunk = 1225. DRLs were consistent with Health Canada recommendations and other Canadian published values, but above international standards. For sites with IR available, less than 50% used this technology routinely for head, chest and trunk exams. Overall, use of IR reduced radiation usage between 11%-32% compared to filtered backprojection, while sites using IR vs IR available used 30%/43% less radiation for head/chest exams (P < .05). No significant difference was observed for abdomen/pelvis exams (P = .385). With the fast pace of CT technical advancement, DRLs should reflect the technology used, instead of just globally applied to anatomical regions. Federal guidelines should be updated at a higher frequency to reflect new technology. In addition, new technologies must be utilised to optimize image quality vs radiation usage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".