Perfusion imaging parameters predict long-term clinical outcome in isolated posterior cerebral artery occlusion stroke patients
Bibliographic record
Abstract
Background Isolated posterior cerebral artery (PCA) occlusions, which account for 5% of ischemic strokes, significantly impact patient quality of life due to effects on the thalamus and visual cortex. Current guidelines for acute treatment and the prognostic utility of perfusion imaging in PCA strokes remain limited and underexplored. Methods We conducted a retrospective analysis of 21 patients with isolated PCA occlusions from January 2017 to March 2023 at two comprehensive medical institutions. Perfusion imaging parameters, including time-to-maximum (Tmax) > 4 s, Tmax > 6 s, Tmax > 8 s, Tmax > 10 s, and mismatch volume, were extracted. The primary outcome was the modified Rankin Scale (mRS) score at 90 days. Results The median age of patients was 70 years, with 62% being male. Time-to-maximum > 4 s volume (rho = 0.46, 95% CI, 0.1–0.71, p = 0.036) and Tmax > 6 s volume (rho = 0.45, 95% CI, 0.09–0.71, p = 0.04) showed significant positive correlations with 90-day mRS scores. Other perfusion parameters, such as Tmax > 8 s volume and mismatch volume, approached statistical significance, while rCBF and hypoperfusion intensity ratio did not show significant correlations. Conclusions Perfusion imaging parameters, particularly Tmax tissue volumes, are correlated with long-term clinical outcomes in patients with isolated PCA occlusions. These findings support the potential role of perfusion imaging in the prognostic assessment and management of PCA stroke patients. Future studies with larger cohorts are warranted to confirm these results and to establish standardized perfusion imaging protocols for PCA occlusions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".