Utility of novel ultra-low field portable MRI in a remote setting in Canada
Bibliographic record
Abstract
OBJECTIVE: The primary objective of this study was to evaluate the feasibility and clinical impact of utilizing low-field portable MRI in a remote setting in Canada. METHODS: This was a single-site prospective cohort study. An ultra-low-field (0.064 T) portable MRI was installed in Weeneebayko General Hospital, Moose Factory, Ontario. Adults presenting with any indication for neuroimaging between November 2021 and June 2023 were eligible for study inclusion. Clinical presentation, indication for imaging,and radiology report turnaround time were recorded. Images were evaluated for diagnostic quality, and radiology reports were analyzed to determine the diagnostic utility of ultra-low-field MRI. RESULTS: An ultra-low-field portable MRI was successfully installed in a remote Canadian location. Fifty patients received a portable MRI scan. Comments on suboptimal image quality were made for 12 (24%) of the portable MRI examinations; however, only 2 (4%) of these were deemed nondiagnostic requiring conventional imaging for further evaluation. Clinically significant pathology was identified in 5 (10%) of the examinations. CONCLUSION: This first-of-its-kind study demonstrates the application of ultra-low-field portable MRI in a remote setting in Canada is feasible and offers clinical information that may help triage which patients require transfer to a center with conventional high-field MRI availability.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".