Bibliographic record
Abstract
MRI is a noninvasive imaging modality that uses powerful electromagnetic and radiofrequency fields to produce cross-sectional images of the body. MRI is primarily used for neurologic exams (28%), followed by musculoskeletal (23%) and oncology exams (17%). In total, 432 MRI units in 11 jurisdictions were identified by the Canadian Medical Imaging Inventory (CMII) in its 2022 to 2023 national survey. Most sites are publicly funded hospitals located in urban centres. Canada has an average of 10.8 MRI units per million people. The greatest density of units per million people is in Yukon, Quebec, and New Brunswick. Overall, 2,214,157 publicly funded MRI examinations were performed in the 2022–2023 fiscal year. This represents a national average of 55.6 exams per 1,000 people, an increase of 4.3% since 2019– 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 MRI exams per 1,000 population. The average age of MRI equipment in Canada is 8.4 years; 62.8% of MRI units are 10 years old or newer, 23.3% are 11 to 15 years old, and 13.9% are more than 15 years old. On average, MRI units operate 15.3 hours per day and 97.4 hours per week. Overall, 76.0% of sites reported MRI operation on weekends and 17% of sites reported operating 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 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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.023 |
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".