Prominent Hypointense Vessel Sign on Susceptibility-Weighted Imaging Is Associated with Clinical Outcome in Acute Ischaemic Stroke
Bibliographic record
Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: The presence of PHVS, CVS, BS and S-D mismatch was significantly higher in the poor outcome group (p < 0.001, p = 0.001, p = 0.013, p = 0.014, respectively). SWI-ASPECTS was significantly lower in the poor outcome group (p = 0.002). Regression analysis revealed SWI-ASPECTS; the presence of PHVS and CVS were independently associated with poor outcome (OR 0.347, p = 0.012; OR 55.77, p = 0.004; OR 58.05, p = 0.005). ROC analysis showed that PHVS had the highest predictive value for poor outcome (AUC 0.783). CONCLUSIONS: 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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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".