Wallerian-Like Degeneration After Ischemic Stroke Revealed by Diffusion - Weighted Imaging
Bibliographic record
Abstract
A 22-year-old female was seen in the emergency within one hour of acute onset of right sided headache followed by weakness of the left side of body. On neurological examination, she was mildly drowsy, had forced right gaze deviation, dysarthria, left hemiplegia and left hemisensory loss. Computed tomography (CT) scan revealed early ischemic changes in the right middle cerebral artery (MCA) territory. The CT angiography done showed evidence of dissection of the supraclinoid segment of the right internal carotid artery with reduced flow distally into the MCA, which was confirmed by a conventional angiogram. In view of the intracranial carotid dissection, the patient was not treated with intravenous tissue plasminogen activator. Magnetic resonance imaging (MRI) of the brain done on the next day revealed evidence of acute ischemic lesions in the right MCA and anterior cerebral artery territory on diffusion-weighted imaging (DWI), with normal brainstem. [Figure 1] A repeat MRI performed 13 days after ictus showed hyperintense signal on DWI in the right cerebral peduncle which was hypointense on apparent diffusion coefficient (ADC) map suggestive of Wallerian-like degeneration. [Figure 2] The signal changes were less conspicuous on T2-weighted images. She had antigravity strength in the left leg but remained weak in her left arm at one month.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".