Predictors of Parenchymal Hematoma After Mechanical Thrombectomy
Bibliographic record
Abstract
Background and Purpose: Parenchymal hematoma (PH) is a rare but dreadful complication of acute ischemic stroke with unclear underlying mechanisms. We aimed to study the incidence and predictors of PH after mechanical thrombectomy. Methods: Data from a prospective observational multicenter registry was screened to identify acute ischemic stroke patients with an anterior circulation large vessel occlusion who underwent mechanical thrombectomy. Clinical, imaging, and procedural characteristics were used for the analysis, including brain imaging systematically performed at 24 hours. PH occurrence was assessed according to ECASS (European Collaborative Acute Stroke Study) criteria. Univariate and multivariable analyses were performed to identify predictors of PH. Results: A total of 1316 patients were included in the study. PH occurred in 153 out of 1316 patients (11.6%) and was associated with a lower rate of favorable outcome and increased mortality. On multivariable analysis, age (per 1 year increase, odds ratio [OR], 1.01; 95% CI, 1.00–1.03; P=0.05), current smoking (OR, 2.02; 95% CI, 1.32–3.09; P<0.01), admission Alberta Stroke Program Early CT Score (per a decrease of 1 point, OR, 1.70; 95% CI, 1.18–2.44; P<0.01), general anesthesia (OR, 1.98; 95% CI, 1.36–2.90; P<0.001), angiographic poor collaterals (OR, 2.13; 95% CI, 1.36–3.33; P<0.001) and embolization in new territory (OR, 2.94; 95% CI, 1.70–5.10; P<0.001) were identified as independent predictors of PH. Conclusions: PH occurred at a rate of 11.6% after mechanical thrombectomy, with high morbidity and mortality. Our study identified clinical, radiological, and procedural predictors of PH occurrence that can serve as the focus of future periprocedural management studies with the aim of reducing its occurrence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".