Evaluation of susceptibility weighted imaging in defining penumbra during acute stage of cerebral infarction and comparison with perfusion weighted imaging
Bibliographic record
Abstract
Objective To evaluate whether susceptibility weighted imaging (SWI) can be used in definition of penumbra during acute stage of cerebral infarction, compared with perfusion weighted imaging (PWI). Methods Ischemic stroke patients within 3 days after onset were included. They adopted multimodal magnetic resonance imaging examination, including regular magnetic resonance imaging sequence (T1WI, T2WI and T2-weight fast fluid-attenuated inversion-recovery), diffusion weighted imaging (DWI), PWI and SWI. Alberta Stroke Programme Early CT Score was done on DWI, SWI and PWI. The mismatch of SWI-DWI (minimal indensity projection (mIP)-DWI) was compared with that of PWI-DWI (mean transit time （MTT）-DWI) and analyzed statistically. The application of prominent vein (PV) on SWI as a sort of alternation of cerebral blood volume (CBV) and direct observation of thrombosis in arteries on SWI were done. Results The SWI-DWI (2.39±1.42) and the MTT-DWI (2.72±1.49) mismatch showed no statistically significant difference (r=0.726，P>0.05). The grade of PV was positively related with the CBV of the ipsilateral brain tissue on admission (r=0.564, P<0.05). SWI showed the similar ability with magnetic resonance angiography to judge responsible blood vessels with susceptibility vessel sign. Conclusion SWI-DWI can evaluate the ischemic penumbra. PV may reflect the increased blood volume of the lesion side of the brain tissue. SWI can reveal the thrombosis of the responsible vessels. Key words: Stroke; Brain ischemia; Magnetic resonance imaging; Diffusion magnetic resonance imaging
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".