Bibliographic record
Abstract
SPECT is a nuclear imaging technique that provides 3D information on functional and molecular processes in the body. SPECT has been integrated with CT to combine the imaging strengths of both modalities in SPECT-CT. In total, 331 SPECT-CT units in 10 provinces and 210 SPECT units operating in 9 provinces were identified by the Canadian Medical Imaging Inventory (CMII) in its 2022–2023 national survey. There are no SPECT-CT and SPECT units operating in Yukon, Northwest Territories, and Nunavut. Canada has 8.3 SPECT-CT units per million people and 5.3 SPECT units per million people. The greatest density of units per million people for SPECT-CT is in Newfoundland and Labrador and the greatest density for SPECT are in Alberta and New Brunswick. The combined volume of SPECT-CT and SPECT exams conducted in 2022–2023 has decreased by approximately 37.5% since 2015, which is attributed to the gradual decommissioning of SPECT units and replacement of these technologies with other imaging modalities. SPECT-CT is primarily used for oncology exams, followed by cardiology exams and musculoskeletal exams. SPECT is primarily used for cardiology exams, followed by oncology and musculoskeletal exams. On average, SPECT-CT and SPECT units operate approximately 42 hours per week across jurisdictions in Canada that have 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".