Changing Radiation Dose from Diagnostic Computed Tomography Examinations in Saskatchewan
Bibliographic record
Abstract
PURPOSE: Follow-up study to observe if provincial mean effective radiation dose for head, chest, and abdomen-pelvis (AP) computed tomographies (CTs) remained stable or changed since the initial 2006 survey. METHODS: Data were collected in July 2008 from Saskatchewan's 13 diagnostic CT scanners of 3358 CT examinations. These data included the number of scan phases and projected dose length product (DLP). Technologists compared projected DLP with 2006 reference data before scanning. Projected DLP was converted to effective dose (ED) for each head, chest, and AP CT. The total dose that the patients received with scans of multiple body parts at the same visit also was determined. RESULTS: The mean (± SD) provincial ED was 3.4 ± 1.6 mSv for 1023 head scans (2.7 ± 1.6 mSv in 2006), 9.6 ± 4.8 mSv for 588 chest scans (11.3 ± 8.9 mSv in 2006), and 16.1 ± 9.9 mSv for 983 AP scans (15.5 ± 10.0 mSv in 2006). Single-phase multidetector row CT ED decreased by 31% for chest scans (9.5 ± 3.9 mSv vs 13.7 ± 9.7 mSv in 2006) and 17% for AP scans (13.9 ± 6.0 mSv vs 16.8 ± 10.6 mSv in 2006) and increased by 19% for head scans (3.2 ± 1.2 mSv vs 2.7 ± 1.5 mSv in 2006). The total patient dose was highest (33.8 ± 10.1 mSv) for the 20 patients who received head, neck, chest, and AP scans during a single visit. Because of increased utilisation and the increased CT head dose, Saskatchewan per capital radiation dose from CT increased by 21% between 2006 and 2008 (1.14 vs 1.38 mSv/person per year). CONCLUSION: Significant dose and variation reduction was seen for single-phase CT chest and AP examinations between 2006 and 2008, whereas CT head dose increased over the same interval. These changes, combined with increased utilisation, resulted in per capita increase in radiation dose from CT between the 2 studies.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".