Temporal trends in spinal imaging in Ontario, 2002-2019 and Manitoba, 2001-2011, Canada
Bibliographic record
Abstract
Abstract Background: Several studies have reported overuse of spinal imaging, which, in Canada, led to several provincial pathways aimed at optimizing use of imaging. We assessed temporal trends in spine imaging in two Canadian provinces. Methods: We explored use of x-ray, computed tomography (CT), and magnetic resonance imaging (MRI) examinations of the cervical, thoracic and lumbar spine regions among adults in Ontario (April 1, 2002 to March 31, 2019) and in Manitoba, Canada (April 1, 2001 to March 31, 2011) using linked Ontario Health Insurance Plan administrative databases and data from Manitoba Health. We calculated the age- and sex-adjusted rates of spinal x-ray, CT and MRI examinations by dividing the number of imaging studies by the population of each province for each year and estimated use of each imaging modality per 100,000 persons. Results: The total cost of spine imaging in Ontario increased from $45.8 million in 2002/03 to $70.3 million in 2018/19 (a 54% increase), and in Manitoba from $2.2 million in 2001/02 to $5 million in 2010/11 (a 127% increase). In Ontario, rates of spine x-rays decreased by 12% and spine CT scans decreased by 28% over this time period, while in Manitoba rates of spine x-rays and CT scans remained constant. Age- and sex-adjusted utilization of spinal MRI scans per 100,000 persons markedly increased over time in both Ontario (277%) and Manitoba (350%). Conclusion: Despite efforts to reduce use of inappropriate spinal imaging, both Ontario and Manitoba have greatly increased utilization of spine MRI in the past two decades.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".