Venous imaging-based biomarkers in acute ischaemic stroke
Bibliographic record
Abstract
Vascular neuroimaging plays a decisive role in selecting the best therapy in patients with acute ischaemic stroke. However, compared with the arterial system, the role of veins has not been thoroughly studied. In this review, we present the major venous imaging-based biomarkers in ischaemic stroke. First, the presence of hypodense veins in the monophasic CT angiography ipsilateral to the arterial occlusion. Second, the asymmetry of venous drainage in the pathological cerebral hemisphere on CT and MRI dynamic angiography. Finally, the presence of hypodense veins on T2* -based MRI. From the physiological point of view, the venous imaging-based biomarkers would detect the alteration of brain perfusion (flow), as well as the optimisation of extraction oxygen mechanisms (misery perfusion). Several studies have correlated the venous imaging-based biomarkers with grade of collateral circulation, the ischaemic penumbra and clinical functional outcome. Although venous imaging-based biomarkers still have to be validated, growing evidence highlights a potential complementary role in the acute stroke clinical decision-making process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".