Prominent Hypointense Vessel Sign on Susceptibility-Weighted Imaging Is Associated with Clinical Outcome in Acute Ischaemic Stroke
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Bibliographic record
Abstract
<b><i>Background:</i></b> Prominent hypointense vessel sign (PHVS) is visualized on susceptibility weighted-imaging (SWI) in acute ischaemic stroke (AIS). We aim to test if PHVS is associated with stroke outcome. <b><i>Methods:</i></b> Forty patients with acute middle cerebral artery occlusion were recruited. The presence of PHVS, cortical vessel sign (CVS), brush sign (BS) and susceptibility-diffuse weighted imaging mismatch (S-D mismatch) and Alberta Stroke Program Early CT Score (ASPECTS) on SWI were compared between the good outcome group (90-day modified Rankin scale [mRS] of 0–2) and the poor outcome group (mRS of 3–6). The receiver operating characteristic curves (ROC) were used to evaluate the predictive ability to poor outcome of above imaging characteristics. <b><i>Results:</i></b> The presence of PHVS, CVS, BS and S-D mismatch was significantly higher in the poor outcome group (<i>p</i> &#x3c; 0.001, <i>p</i> = 0.001, <i>p</i> = 0.013, <i>p</i> = 0.014, respectively). SWI-ASPECTS was significantly lower in the poor outcome group (<i>p</i> = 0.002). Regression analysis revealed SWI-ASPECTS; the presence of PHVS and CVS were independently associated with poor outcome (OR 0.347, <i>p</i> = 0.012; OR 55.77, <i>p</i> = 0.004; OR 58.05, <i>p</i> = 0.005). ROC analysis showed that PHVS had the highest predictive value for poor outcome (AUC 0.783). <b><i>Conclusions:</i></b> The presence of PHVS, CVS and SWI-ASPECTS were associated with poor outcome in AIS. The presence of PHVS was the most effective radiographic marker for predicting outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it