Sci‐Thur PM – Colourful Interactions: Highlights 07: Canadian Computed Tomography Survey: National Diagnostic Reference Levels
Bibliographic record
Abstract
Purpose: The Canadian Computed (CT) Tomography Survey sought to collect CT technology and dose index data (CTDI and DLP) at the national level in order to establish national diagnostic reference levels (DRLs) for seven common CT examinations of standard‐sized adults and pediatric patients. Methods: A single survey booklet (consisting of four sections) was mailed to and completed for each participating CT scanner. Survey sections collected data on (i) General facility and scanner information, (ii) routine protocols (as available), (iii) individual patient data (as applied) and (iv) manual CTDI measurements. Results: Dose index (CTDIvol and DLP) and associated patient data from 24 280 individual patient exam sequences was analyzed for seven common CT examinations performed in Canada: Adult Head, Chest, Abdomen/Pelvis, and Chest/Abdomen/Pelvis, and Pediatric Head, Chest, and Abdomen. Pediatric examination data was sub‐divided into three age ranges: 0–3, 3–7 and 7–13 years. DRLs (75th percentile of dose index distributions) were found for all thirteen groups. Further analysis also permitted segmentation of examination data into 8 sub‐groups, whose dose index data was displayed along with group histograms – showing relative contribution of axial vs. helical, contrast use (C+ vs. C‐), and application of fixed current vs. dose reduction (DR) – 75th percentiles of DR sub‐groups were, in almost all cases, lower than whole group (examination) DRLs. Conclusions: The analysis and summaries presented in the pending survey report can serve to aid local CT imaging optimization efforts within Canada and also contribute further to international efforts in radiation protection of patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.152 | 0.026 |
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".