P.097 Subarachnoid hemorrhage associated with a thromboembolic ischemic stroke- an unexpected observation
Bibliographic record
Abstract
Atherosclerosis is a significant risk factor for ischemic stroke, and is a frequent cause for extra- and intra-cranial vessels stenosis. Here, we present an unusual case of ischemic stroke associated with intra-cranial vessel stenosis and subarachnoid hemorrhage (SAH) secondary to carotid artery atheroma. A 64-year old female known for hypertension and dyslipidemia presented with a three-day history of three transient episodes (< 30 minutes) of dysarthria and right hand weakness. An initial brain CT scan revealed left frontal SAH. She was admitted to our Stroke Unit for observation and management. CT-angiogram revealed 90% ICAs stenosis bilaterally with several short focal stenotic lesions, involving several left MCA branches. Brain MRI revealed acute infarcts in the left insula, external capsule and inferior frontal gyrus. The clinical picture was attributed to a thromboembolic left MCA ischemic stroke. She was managed with maximum medical therapy, and later underwent successful left carotid endarterectomy. The presence of SAH on our patient’s presenting CT scan lead to an initial radiological diagnosis of RCVS. However, subsequent imaging studies indicated that SAH had occurred in association with a thromboembolic ischemic stroke. Despite its rarity, this clinical association is important to recognize to avoid diagnostic confusion and guide appropriate management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".