Perfusion imaging predicts short‐term clinical outcome in isolated posterior cerebral artery occlusion stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Ischemic strokes due to isolated posterior cerebral artery (PCA) occlusions represent 5% of all strokes but have significant impacts on patients' quality of life, primarily due to visual deficits and thalamic involvement. Current guidelines for acute PCA occlusion management are sparse, and the prognostic value of perfusion imaging parameters remains underexplored. METHODS: We conducted a retrospective analysis of 32 patients with isolated PCA occlusions treated at Johns Hopkins Medical Institutions between January 2017 and March 2023. Patients underwent pretreatment perfusion imaging, with perfusion parameters analyzed using RAPID software. The primary outcome was short-term clinical outcome as measured by the National Institutes of Health Stroke Scale (NIHSS) at discharge. RESULTS: The median age of the cohort was 70 years, with 34% female and 66% male. Significant correlations were found between NIHSS at discharge and various perfusion parameters, including time-to-maximum (Tmax) >6 seconds (ρ = .55, p = .004), Tmax >8 seconds (ρ = .59, p = .002), Tmax >10 seconds (ρ = .6, p = .001), mismatch volume (ρ = .51, p = .008), and cerebral blood volume (CBV) < 34% (ρ = .59, p = .002). CONCLUSIONS: Tmax and CBV volumes significantly correlated with discharge NIHSS with marginal superiority of Tmax >10 seconds and CBV <42% volumes. These findings suggest that CT and MR perfusion imaging can play a crucial role in the acute management of PCA strokes, though larger, standardized studies are needed to validate these results and refine imaging thresholds specific to posterior circulation infarcts.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".