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.
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 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 | 0.004 | 0.004 |
| 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".