Survey of clinical doses from computed tomography examinations in the Canadian province of Manitoba
Bibliographic record
Abstract
The purpose of this study was to document CT doses for common CT examinations performed throughout the province of Manitoba. Survey forms were sent out to all provincial CT sites. Thirteen out of sixteen (81 %) sites participated. The authors assessed scans of the brain, routine abdomen-pelvis, routine chest, sinuses, lumbar spine, low-dose lung nodule studies, CT pulmonary angiograms, CT KUBs, CT colonographies and combination chest-abdomen-pelvis exams. Sites recorded scanner model, protocol techniques and patient and dose data for 100 consecutive patients who were scanned with any of the aforementioned examinations. Mean effective doses and standard deviations for the province and for individual scanners were computed. The Kruskal-Wallis test was used to compare the variability of effective doses amongst scanners. The t test was used to compare doses and their provincial ranges between newer and older scanners and scanners that used dose saving tools and those that did not. Abdomen-pelvis, chest and brain scans accounted for over 70 % of scans. Their mean effective doses were 18.0 ± 6.7, 13.2 ± 6.4 and 3.0 ± 1.0 mSv, respectively. Variations in doses amongst scanners were statistically significant. Most examinations were performed at 120 kVp, and no lower kVp was used. Dose variations due to scanner age and use of dose saving tools were not statistically significant. Clinical CT doses in Manitoba are broadly similar to but higher than those reported in other Canadian provinces. Results suggest that further dose reduction can be achieved by modifying scanning techniques, such as using lower kVp. Wide variation in doses amongst different scanners suggests that standardisation of scanning protocols can reduce patient dose. New technological advances, such as dose-reduction software algorithms, can be adopted to reduce patient dose.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".