Sci—Thur PM: Imaging — 06: Canada's National Computed Tomography (CT) Survey
Bibliographic record
Abstract
The value of computed tomography (CT) in medical imaging is reflected in its' increased use and availability since the early 1990's; however, given CT's relatively larger exposures (vs. planar x‐ray) greater care must be taken to ensure that CT procedures are optimised in terms of providing the smallest dose possible while maintaining sufficient diagnostic image quality. The development of CT Diagnostic Reference Levels (DRLs) supports this process. DRLs have been suggested/supported by international/national bodies since the early 1990's and widely adopted elsewhere, but not on a national basis in Canada. Essentially, CT DRLs provide guidance on what is considered good practice for common CT exams, but require a representative sample of CT examination data to make any recommendations. Canada's National CT Survey project, in collaboration with provincial/territorial authorities, has collected a large national sample of CT practice data for 7 common examinations (with associated clinical indications) of both adult and pediatric patients. Following completion of data entry into a common database, a survey summary report and recommendations will be made on CT DRLs from this data. It is hoped that these can then be used by local regions to promote CT practice optimisation and support any dose reduction initiatives.
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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 | 0.006 |
| Science and technology studies | 0.003 | 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) | 0.028 | 0.006 |
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".