Impact of collateral flow on tissue fate in acute ischaemic stroke
Bibliographic record
Abstract
BACKGROUND: Collaterals may sustain penumbra prior to recanalisation yet the influence of baseline collateral flow on infarct growth following endovascular therapy remains unknown. METHODS: Consecutive patients underwent serial diffusion and perfusion MRI before and after endovascular therapy for acute cerebral ischaemia. We assessed the relationship between MRI diffusion and perfusion lesion indices, angiographic collateral grade and infarct growth. Tmax perfusion lesion maps were generated and diffusion-perfusion mismatch regions were divided into Tmax >or=4 s (severe delay) and Tmax >or=2 but <4 s (mild delay). RESULTS: Among 44 patients, collateral grade was poor in 7 (15.9%), intermediate in 20 (45.5%) and good in 17 (38.6%) patients. Although diffusion-perfusion mismatch volume was not different depending on the collateral grade, patients with good collaterals had larger areas of milder perfusion delay than those with poor collaterals (p = 0.005). Among 32 patients who underwent day 3-5 post-treatment MRIs, the degree of pretreatment collateral circulation (r = -0.476, p = 0.006) and volume of diffusion-perfusion mismatch (r = 0.371, p = 0.037) were correlated with infarct growth. Greatest infarct growth occurred in patients with both non-recanalisation and poor collaterals. Multiple regression analysis revealed that pretreatment collateral grade was independently associated with infarct growth. CONCLUSION: Our data suggest that angiographic collateral grade and penumbral volume interactively shape tissue fate in patients undergoing endovascular recanalisation therapy. These angiographic and MRI parameters provide complementary information about residual blood flow that may help guide treatment decision making in acute cerebral ischaemia.
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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 | 0.000 | 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) | 0.001 | 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".