Bibliographic record
Abstract
PET-CT Imaging PET-CT is an advanced imaging technique that combines PET with CT to measure metabolic or biochemical activity in the human body. Sixty PET-CT units in 9 provinces were identified by the Canadian Medical Imaging Inventory (CMII) in its 2022–2023 national survey. All units are located in urban centres. Canada has 1.5 PET-CT units per million people. The greatest density of units per million people is in Quebec, New Brunswick, and Newfoundland and Labrador. Approximately 156,320 publicly funded PET-CT examinations were performed in the 2022–2023 fiscal year. This represents a national average of 3.9 exams per 1,000 people, an increase of 18.2% since 2019– PET-CT was reported to have the largest demand in oncology (66.1%), followed by cardiology (13.2%) and neurology (10.1%). Applications for PET-CT continue to expand to new clinical indications and therapeutic areas. The upfront capital and ongoing operational costs of PET-CT units, as well as the costs of radiopharmaceutical products and equipment, may act as barriers to the rapid adoption of this technology. Canada is positioned in the bottom 25% of Organisation for Economic Co-operation and Development (OECD) countries in units per million population and the bottom 50% of OECD countries for average volume of publicly funded PET-CT exams per 1,000 population. The average age of PET-CT equipment in Canada is 7.2 years; 51.5% of PET-CT units are 5 years old or newer, 21.2% are 6 to 10 years old, and 27.3% are more than 10 years old. Radiotracers are essential in PET imaging. The most commonly used PET-CT radiotracers are for oncology: 91% of sites reported using fluorodeoxyglucose F18 and 31.8% of sites reported using gallium-68 DOTA-TATE. The production of most radiotracers requires the use of a cyclotron.1 Overall, 21.7% of PET-CT sites reported local proximity to a cyclotron, reflecting a disparity in the access to and growing demand for radiotracer supply. PET-MRI Imaging PET-MRI is a technique that combines PET with MRI to produce highly detailed imaging of soft tissues in the human body. PET-MRI is almost exclusively used for research purposes in Canada; therefore, data are limited for this modality. Six PET-MRI units were identified in Canada across 3 provinces, representing a national average of 0.2 units per million people. The average age of PET-MRI equipment in Canada is 6.7 years; 2 units are 6 to 10 years old, and 1 unit is 5 years old.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.020 |
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".