Sensitivity of Magnetic Resonance Imaging of the Medial Longitudinal Fasciculus in Internuclear Ophthalmoplegia
Bibliographic record
Abstract
BACKGROUND: Internuclear ophthalmoplegia (INO) is a result of insult to the medial longitudinal fasciculus (MLF). Clinicoradiological correlation in patients with INO has been reported to be poor; however, prior studies have used low resolution MRI imaging techniques and included patients with subclinical INO. We aimed to determine the sensitivity of modern MRI interpreted by a specialist neuroradiologist to detect clinically evident INO. METHODS: A retrospective chart review of patients in 2 tertiary University-affiliated neuro-ophthalmology practices with the diagnosis of INO. MRI scans of all patients were reviewed and interpreted by a fellowship-trained neuroradiologist for the presence of lesion in MLF and concordance with the original imaging report. RESULTS: Forty-five patients were included in the study: 33 with demyelinating disease, 11 with stroke, and 1 with intracranial mass. A visible MLF lesion was present in 25/33 demyelinating cases and 7/11 ischemic cases. Lesions in 2 cases in each group were identified only after review by a fellowship-trained neuroradiologist. In demyelinating INO, patients with a visible MLF lesion were more likely to show other brainstem (72%) and supratentorial (51%) white matter lesions. CONCLUSIONS: In 25% of patients with demyelinating INO and 33% of patients with ischemic INO, no visible lesion was identified on current high-quality MRI imaging. Review of imaging by a neuroradiologist increased the possibility of lesion been identified.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".