Feasibility and Cost Analysis of Portable MRI Implementation in a Remote Setting in Canada
Bibliographic record
Abstract
OBJECTIVE: To conduct feasibility and cost analysis of portable MRI implementation in a remote setting where MRI access is otherwise unavailable. METHODS: Portable MRI (ultra-low field, 0.064T) was installed in Weeneebayko General Hospital, Moose Factory, Ontario. Adult patients, presenting with any indication for neuroimaging, were eligible for study inclusion. Scanning period was from November 14, 2021, to September 6, 2022. Images were sent via a secure PACS network for Neuroradiologist interpretation, available 24/7. Clinical indications, image quality, and report turnaround time were recorded. A cost analysis was conducted from a healthcare system's perspective in 2022 Canadian dollars, comparing cost of portable MRI implementation to transporting patients to a center with fixed MRI. RESULTS: Portable MRI was successfully implemented in a remote Canadian location. Twenty-five patients received a portable MRI scan. All studies were of diagnostic quality. No clinically significant pathologies were identified on any of the studies. However, based on clinical presentation and limitations of portable MRI resolution, it is estimated that 11 (44%) of patients would require transfer to a center with fixed MRI for further imaging workup. Cost savings were $854,841 based on 50 patients receiving portable MRI over 1 year. Five-year budget impact analysis showed nearly $8 million dollars saved. CONCLUSIONS: Portable MRI implementation in a remote setting is feasible, with significant cost savings compared to fixed MRI. This study may serve as a model to democratize MRI access, offer timely care and improved triaging in remote areas where conventional MRI is unavailable.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".