Enhancement of the Third Cranial Nerve due to Microvascular Ischemia: Case Report
Bibliographic record
Abstract
Third nerve palsy (3NP) commonly results from a microvascular ischemic insult. Typically, computed tomography or magnetic resonance angiography is performed to rule out a posterior communicating artery aneurysm. If this is normal and the pupil is spared, patients are often observed with the expectation of spontaneous improvement within 3 months. Oculomotor nerve enhancement on MRI with contrast in the context of microvascular 3NP is not well recognized. Here, we report third nerve enhancement in a case of a 67-year-old woman with diabetes and other vascular risk factors who presented with left eye ptosis and a limitation of extraocular eye movements consistent with 3NP. She underwent an extensive inflammatory workup that was negative and the diagnosis of a microvascular 3NP was made. A spontaneous recovery was achieved within 3 months, and she did not receive any treatment. She remained clinically well, although increased T2 signal in the oculomotor nerve persisted after 10 months. While the exact mechanism remains unknown, it is likely that microvascular ischemic insults lead to intrinsic changes of the third nerve that may result in enhancement and persistent T2 signal. Additional workup for inflammatory causes of 3NP may not be required when enhancement of the oculomotor nerve is seen in the right clinical context. Further study is required to understand why enhancement is a rarely reported finding in patients with microvascular ischemic 3NP.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".