Could MRI and CT Scanners be Operated More Intensively in Canada
Bibliographic record
Abstract
Although availability of necessary equipment could play a role in wait times for MRI and CT exams in Canada, there are other dimensions to this issue. More machines do not necessarily reduce wait times. It is important to also consider other factors like the level of utilisation of the existing pool of scanners. This paper analyses utilisation of MRI and CT scanners (machines) by focussing on two indicators: the number of exams per machine per year and the number of hours of operation per machine per week. These values were calculated and reported by province, followed by an assessment of the average level of utilisation of MRI and CT scanners in Canada. The findings suggest that some provinces use their MRI or CT scanners less intensively than others. On average, in Canada, an additional 31% operating capacity may exist for MRI and 68% for CT without additional capital/infrastructure investments. However, supply-side as well as demand-side constraints may prevent a given jurisdiction from operating at full capacity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".