Neuroimaging predictors of malignant brain oedema after thrombectomy in ischemic stroke: a systematic review and meta-analysis
Bibliographic record
Abstract
Background We systematically reviewed neuroimaging predictors for malignant brain oedema (MBE) after thrombectomy in patients with ischemic stroke.Methods We searched MEDLINE and EMBASE in November 2023 for studies of patients with ischemic stroke. We included studies investigating neuroimaging predictors or prediction models for MBE after thrombectomy. We estimated effect size for the association between predictors and MBE by odds ratios (ORs) or standardized mean differences (SMDs), and pooled results using random-effects modelling.Results We included 19 studies (n = 6007) with 17 neuroimaging factors and 5 models. Lower Alberta Stroke Program Early CT scores (ASPECTS, n = 3052, SMD −1.84, 95% CI −2.52 – −1.16; df = 9) and longer extent of arterial occlusion at baseline were associated with higher risk of MBE. Post-thrombectomy ASPECTS was associated with MBE in general stroke patients (n = 453, SMD −2.91, −4.02 – −1.79; df = 1), but not in successfully reperfused patients (n = 110, SMD 0.24, −0.16 – 0.65). Successful reperfusion reduced risk of MBE (n = 4851, OR 0.39, 0.30–0.51; df = 13). Contrast enhancement on CT after thrombectomy was associated with higher risk of MBE (n = 998, OR 4.82, 2.53–9.20; df = 4). More reserved brain volume capacity (baseline: n = 683, OR 0.83, 0.77–0.91, p < .001; post-thrombectomy: n = 329, OR 0.53, 0.37–0.77, p < .001) and good collaterals (baseline: n = 2301, OR 0.14, 0.10–0.20, df = 3; post-thrombectomy: n = 1006, OR 0.28, 0.15–0.51; df = 2) were associated with lower risk of MBE.Conclusion Lower ASPECTS and longer arterial occlusion at baseline, and post-thrombectomy CT contrast enhancement increased risk of MBE. Reperfusion after thrombectomy, more reserved brain volume and good collaterals at baseline and post-thrombectomy reduced its risk.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".