Bibliographic record
Abstract
CT is an imaging technique that uses X-rays, sensitive radiation detectors, and computer analysis to produce cross-sectional images to detect abnormalities in areas of the body. CT was reported to have the largest demand in oncology (24%), followed by neurology (16.5%) and hepatobiliary (14%) use. Applications for CT continue to expand to new clinical indications and therapeutic areas. Five hundred and sixty CT units were identified by the CMII in its 2022–2023 national survey, with all provinces and territories reporting CT capacity. Most sites are publicly funded hospitals located in urban settings. Canada has an average of 14 CT units per million people. The greatest density of units per million people is in Newfoundland and Labrador, New Brunswick, Yukon, Northwest Territories and Nunavut. Canada is positioned in the bottom 15% of OECD countries in units per million population and the top 45% of OECD countries for average volume of publicly funded CT exams per1,000 population. Approximately 6.4 million publicly funded CT examinations were performed in the 2022–2023 fiscal year. This represents a national average of 160.2 exams per 1,000 people, an increase of 9% since 2015. The average age of CT equipment in Canada is 8.2 years; 29.8% of CT units are 5 years old or newer, 36.7% are 6 to 10 years old, and 33.4% are more than 10 years old. The adoption and use of radiation safety strategies are increasing in Canada. An increasing number of CT units are now equipped with dose-management controls and patient dose recording features. Approximately 30% of CT units are reported to have dual-target imaging. CT units operate an average of 14 hours per day. Approximately 80% of sites reported CT operation on weekends and 40% of sites reported CT operation 24 hours a day.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".