Diagnostic Puzzle of Acute Ischemic Stroke Mimics – Seizure Versus Post-Stroke Recrudescence: A Case Report
Bibliographic record
Abstract
BACKGROUND Focal seizure with impaired awareness, post-seizure Todd's phenomenon, and post-stroke recrudescence can all present with focal neurological deficits, mimicking stroke. As acute ischemic stroke mimics, they are distractors in the emergency setting where management is time-sensitive both for seizure and stroke. Nevertheless, a timely diagnosis can be made with exploration of the clinical features supported by investigation such as computerized tomographic perfusion. CASE REPORT Our patient was a 65-year-old woman who was known hypertensive, with type 2 diabetes mellitus, and previous intracerebral hemorrhage with minimal right-sided residual deficits, but still able to ambulate independently. She was brought to the Emergency Department because 1 hour prior to presentation, she had sudden worsening of weakness of the right limbs, aphasia, aggression, and confusion. An initial impression of repeat acute stroke, focal seizure with impaired awareness, Todd's phenomenon, and post-stroke recrudescence was considered. While CT angiography was suggestive of left middle cerebral artery occlusion, CT perfusion revealed extensive hypoperfusion patterns beyond the region of the occlusion, thus suggesting a different etiology from acute ischemic stroke. In view of her previous left hemispheric lesion coupled with the presentation, our working diagnosis was seizure with Todd's phenomenon, and she was started on an anti-epileptic drug. Her condition returned to baseline within 24 h of admission and was subsequently discharged. CONCLUSIONS Our case demonstrates that adequate elucidation of clinical features in conjunction with CT perfusion, as a dual-purpose tool, can aid the diagnosis of both stroke mimics and acute ischemic stroke in the Emergency Department where rapid treatment is essential.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".