Fate of the Penumbra after Mechanical Thrombectomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In acute stroke, CTP is often used to visualize the endangered brain areas, including the ischemic core and the penumbra. Our goal was to assess the evolution of the infarct after mechanical thrombectomy and to analyze the interventional factors determining the fate of the penumbra. MATERIALS AND METHODS: All patients receiving mechanical thrombectomy in the anterior circulation and receiving CTP beforehand were identified. The infarct volume was specified. The clinical parameters, outcome, and interventional results were correlated with the CTP and the final infarct size. RESULTS: In total, 73 patients were included. After mechanical thrombectomy, 78.1% reached a TICI score of 3/2b. The final infarct volume was significantly smaller, with a TICI score of 3/2b compared with less sufficient recanalization (19.60 ± 3 cm(3) versus 38.1 ± 9 cm(3); P < .001). After TICI 3/2b recanalization, 81% ± 5.2% of the potential infarct size (calculated as the sum of infarct core and penumbra) could be rescued. In patients with TICI scores of 2a or worse, only 39 ± 28.3 were salvaged (P < .001). The Alberta Stroke Program Early CT Score after successful recanalization TICI score of 3/2b resulted in a decline of 1.9 ± 1.4 compared with the significantly higher degradation score of 3.7 ± 1.7 after recanalization, with a TICI score of 2a or worse. A recanalization TICI score of 3/2b resulted in an NIHSS improvement of 7.3 ± 0.8 NIHSS points, whereas a poorer recanalization improved on the NIHSS by only 2.5 ± 1.5 points (P < .01). CONCLUSIONS: Mechanical thrombectomy is a potent method to rescue large areas of penumbra in acute stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".