Posterior spinal artery aneurysm as an unlikely culprit for perimesencephalic pattern subarachnoid hemorrhage: illustrative case
Bibliographic record
Abstract
BACKGROUND: Angiogram-negative nontraumatic subarachnoid hemorrhage (SAH) can be diagnostically challenging, and a broad differential diagnosis must be considered. Particular attention to initial radiographic hemorrhage distribution is essential to guide adjunctive investigations. Posterior spinal artery aneurysms are rare clinical entities with few reported cases in the literature. An understanding of isolated spinal artery aneurysm natural history, diagnosis, and management is evolving as more cases are identified. OBSERVATIONS: Isolated thoracic posterior spinal artery aneurysm can be the culprit lesion in perimesencephalic distribution SAH. Embolization resulted in complete aneurysm occlusion and did not result in periprocedural morbidity. At the 1-year follow-up, the patient was neurologically intact with no recurrence on magnetic resonance angiography. LESSONS: This case report highlighted the presentation, diagnostic workup, clinical decision-making, and endovascular intervention for a woman who presented with SAH secondary to posterior spinal artery aneurysm. After initially negative results on vascular imaging, dedicated spinal vascular imaging revealed the location of the aneurysm. Multiple treatment modalities exist for isolated spinal artery aneurysms and must be selected on the basis of patient- and lesion-specific characteristics.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".