Edema Reduction versus Penumbra Salvage: Investigating Treatment Effects of Mechanical Thrombectomy in Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVE: Mechanical thrombectomy (MT) is of benefit to patients with ischemic stroke; however, the effect of recanalization on lesion pathophysiology is not yet well understood. The aim of this study was to quantitatively assess how the effect of vessel recanalization on clinical outcome is mediated by edema reduction versus penumbra salvage. METHODS: Consecutive analysis was made of anterior circulation ischemic stroke patients triaged by multimodal computed tomography (CT) undergoing MT. Edema reduction was defined using the difference of quantitative net water uptake (NWU) determined on baseline and follow-up CT (∆NWU). Penumbra salvage volume (PSV) was defined as the difference between admission penumbra and net infarct growth volumes to follow-up. Mediation analyses were performed with vessel recanalization as independent variable (modified Thrombolysis in Cerebral Infarction ≥ 2b) and ∆NWU/PSV as mediator variables. Modified Rankin Scale scores at 90 days served as endpoint. RESULTS: Of 422 included patients, 321 (76%) achieved successful recanalization. The median ∆NWU was 6.8% (interquartile range [IQR] = 3.9-10.4), and the median PSV was 66ml (IQR = 8-124). ∆NWU, PSV, and recanalization were significantly associated with functional outcome in logistic regression analysis. ∆NWU and PSV partially mediated the relationship between recanalization and outcome. Sixty-six percent of the relationship between recanalization and functional outcome could be explained by treatment-induced edema reduction, whereas 22% was mediated by PSV (p < 0.0001). INTERPRETATION: Compared to penumbra salvage, edema reduction was a stronger mediator of the effect of recanalization on functional outcome. Given the current trials on adjuvant neuroprotectants also targeting ischemic edema formation, combining reperfusion with antiedematous neuroprotectants may have synergistic effects resulting in better outcomes in patients with ischemic stroke. ANN NEUROL 2023.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".