Cost-Effectiveness Analysis of a Reduction in Diagnostic Imaging in Degenerative Spinal Disorders
Bibliographic record
Abstract
BACKGROUND: Advanced imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are highly sensitive, but often non-specific, diagnostic tools. Despite this, CT and MRI are overutilized in degenerative spinal disorder diagnosis. From the perspective of the Ministry of Health, we evaluated against usual care the cost-effectiveness of a hypothetical triage program for non-emergent spinal disorders that reduces unnecessary imaging uses. METHODS: Diagnostic and surgical data were prospectively collected on 2,046 outpatients who received consultation with the senior surgical author at Toronto Western Hospital, University Health Network, between September 2005 and April 2008. Using these data, we modelled an evidence-based diagnostic triage program wherein spine-focused clinical assessments and plain X-ray imaging would be applied prior to CT and MRI. Incremental costs were the incurred expenses from additional consultations and plain X-rays less the cost savings from the eliminated CT and MRI scans, expressed in 2009 Canadian dollars. Outcomes were expressed as the number of surgical candidates identified per MRI used in diagnosis, reflecting the efficiency of diagnostic imaging. RESULTS: The triage program incurred $109,720 from additional consultations and plain X-rays and saved $2,117,697 from eliminated CT and MRI scans, resulting in net cost savings of $2,007,977 for the 31 months of the study period, or $777,282 per year. In usual care, 0.328~0.418 surgical candidates were identified per MRI whereas in the triage program, 0.736~0.885 surgical candidates were identified per MRI, resulting in over a twofold improvement in MRI efficiency. The triage program was therefore dominating. Applying to high-volume spine surgeons in Ontario, we estimated that the implementation of the triage program would save the province $24,234,929 per year. INTERPRETATION: Based on the assumptions made in our modelling, eliminating unnecessary imaging in spinal disorder diagnosis can save healthcare significant resources.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".