Susceptibility-weighted imaging in post-treatment evaluation in the early stage in patients with acute ischemic stroke
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the association between abnormal signs on susceptibility-weighted imaging (SWI) and post-treatment outcome in the early stage in patients with acute ischemic stroke. METHODS: Thirty-seven patients with middle cerebral artery territory infarction were recruited. Baseline and 24-hour follow-up magnetic resonance imaging was performed. Pre- and 24-hour post-treatment clinical conditions were assessed with the National Institutes of Health Stroke Scale (NIHSS) score. Prominent vessel sign (PVS) on SWI and infarcted areas on diffusion-weighted imaging (DWI) were assessed using the Alberta Stroke Program Early CT (ASPECT) score system. Susceptibility vessel sign (SVS) was evaluated and recorded. The associations between image abnormalities and clinical scores were analyzed. RESULTS: PVS was found in 35 patients and SVS in seven patients. The extent of PVS was significantly correlated with the post-treatment DWI ASPECT score (r = 0.79), but not with the post-treatment NIHSS score or the post-pre NIHSS difference score. The presence of SVS was significantly correlated with the post-treatment NIHSS score (r = 0.41). CONCLUSION: PVS might be a useful predictor of early imaging prognosis and infarct growth in patients with acute ischemic stroke. SVS is related to a poor early outcome and could be useful for assessing stroke.
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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 | 0.000 |
| 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".