Noninvasive assessment of ischemic penumbra by using MR-SWI during the acute phase of cerebral infarction: a comparison to PWI
Bibliographic record
Abstract
Background: Assessment of ischemic penumbra during the acute stage of cerebral infarction is crucial for a decision to initiate thrombolytic therapy and for predicting stroke evolution. Although controversial as a perfect equivalence to penumbra, perfusion weighted imaging (PWI)-diffusion weighted imaging (DWI) mismatch may predict the response to thrombolysis. Due to the reliance on contrast agents in PWI, noninvasive alternatives remain an unmet need. Methods: We herein investigate the potentials of SWI as an alternative to PWI in defining ischemic penumbra and in predicting stroke outcome. A multimodal magnetic resonance imaging work-up which includes conventional magnetic resonance imaging sequences (T1WI, T2WI and FLAIR), DWI, PWI and SWI was performed. The Alberta Stroke Programme Early CT Score (ASPECTS) was used to evaluate the changes in DWI, SWI and PWI. Results: The mismatch of SWI-DWI was comparable with that of PWI-DWI (p>0.05). Furthermore, the grade of prominent vein and the cerebral blood volume in the ipsilateral brain tissue were positively correlated. Conclusions: SWI can be used as a noninvasive alternative to identify occlusive arteries and to evaluate the ischemic penumbra. The susceptibility vein sign may represent thrombosis in arteries whereby being helpful to identify responsible blood vessels in ischemic 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".