The Usefulness of Gadolinium-Enhanced Images on a Follow-up Magnetic Resonance Image in Suspected Multiple Sclerosis
Bibliographic record
Abstract
PURPOSE: Multiple sclerosis diagnostic criteria include the presence of gadolinium-enhancing lesions when determining dissemination in space and time. Gadolinium is expensive, increases scan time and patient discomfort, and can, rarely, cause serious adverse effects. Our objective was to determine the usefulness of including gadolinium-enhanced images as part of a follow-up brain magnetic resonance imaging (MRI) in patients with a clinically isolated syndrome. METHODS: Consecutive patients seen between 2008 and 2010 with a clinically isolated syndrome suggestive of multiple sclerosis were prospectively enrolled, had a non-gadolinium-enhanced brain MRI, and consented to a follow-up gadolinium-enhanced brain MRI. The primary outcome was a comparison of the number of patients diagnosed with multiple sclerosis compared with the number who would have been diagnosed without the gadolinium-enhanced images. RESULTS: Twenty-one patients enrolled, and 2 withdrew. Follow-up MRIs were performed a median of 241 days after the initial MRI. Eleven patients met the primary outcome and were diagnosed with multiple sclerosis: 6 as a result of a second clinical attack and 5 by using imaging criteria for dissemination in space and time. If the gadolinium-enhanced images had not been obtained, then there would have been no change in the primary outcome. CONCLUSIONS: In Canadian centers with similar MRI waiting times to those in our study, the routine use of gadolinium as part of a follow-up MRI in patients with suspected multiple sclerosis may not be clinically useful. Gadolinium-enhanced images could still be obtained on an as-needed basis for specific clinical indications.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".