Permeability-surface area product of the penumbra as a predictor of outcome after endovascular treatment of anterior circulation acute ischemic stroke
Bibliographic record
Abstract
Background Permeability-surface product is a predictor of blood–brain barrier disruption, a condition that may be related to higher likelihoods of hemorrhagic transformations in acute stroke. Purpose To investigate whether permeability-surface product can be used as a parameter for predicting outcome after mechanical thrombectomy in patients with anterior circulation acute ischemic stroke. Material and Methods We retrospectively identified patients with acute middle cerebral artery stroke who underwent successful mechanical thrombectomy between November 2009 and July 2015. Multiple parameters (including age) and CT perfusion-related parameters (including permeability-surface product) were compared between patients with favorable (modified Rankin Scale [mRS] = 0–2) and unfavorable (mRS > 2) outcome. Results Thirty patients were included, 50% having favorable and 50% having unfavorable outcome. Younger age was significantly associated with favorable outcome ( P < 0.03). Other baseline characteristics, such as size of CT perfusion core infarction, perfusion abnormality, and presentation of subcortical infarction were not significantly different between groups. No significant difference was observed between groups for permeability-surface product in the ipsilateral penumbra or for the ratio between permeability-surface product penumbra value and contralateral normal brain (permeability-surface product ratio). Conclusions No significant difference was observed between patients with and without favorable outcome after mechanical thrombectomy for either permeability-surface product value or permeability-surface product ratio. Although permeability-surface product is a good predictor of blood–brain barrier disruption, this study revealed no evidence that either permeability-surface product value or permeability-surface product ratio is associated with future change in the penumbra.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".