The lesion core extent modulates the impact of early perfusion mismatch imaging on outcome variability after thrombectomy in stroke
Bibliographic record
Abstract
Introduction: Despite profitable group effects on functional outcomes after mechanical thrombectomy (MT) in large vessel occlusion (LVO), many patients with successful reperfusion show a non-favorable long-term outcome, highlighting the necessity to identify potential biomarkers predicting outcome variability. In this regard, the role of perfusion mismatch imaging for outcome variability in the early time window within 6 h after symptom onset is a matter of debate. We attempted to investigate under which conditions early perfusion mismatch imaging accounts for variability in functional outcomes after mechanical thrombectomy. Patients and methods: In a retrospective single-center study, we examined 190 consecutive patients with LVO who were admitted to the Medical Center Lübeck within 6 h after symptom onset, all of whom underwent MT. Perfusion mismatch was quantified by applying the Alberta Stroke Program Early CT score (ASPECTS) on CT-measured cerebral blood flow (CBF-ASPECTS) and subtracting it from an ASPECTS application on cerebral blood volume (CBV-ASPECTS), i.e., ASPECTS mismatch. Using multivariate ordinal regression models, associations between ASPECTS mismatch and modified Rankin Scale (mRS) after 90 days were assessed. Furthermore, the interaction between ASPECTS mismatch and the core lesion volume was calculated to evaluate conditional associations. Results: -value < 0.001], did show a significant association with functional outcomes. Model comparisons revealed that, profoundly, in patients with large core lesion volumes (CBV-ASPECTS < 6 or NCCT-ASPECTS < 6), perfusion mismatch showed a negative correlation with the mRS. Discussion and conclusion: Perfusion mismatch imaging within the first 6 h of symptom onset provides valuable insights into the outcome variability of LVO stroke patients receiving thrombectomy but only in patients with large ischemic core lesions.
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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 | 0.003 |
| 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 | 0.001 | 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".