Minimally Invasive Surgery Versus Open Craniotomy With Clot Evacuation After Intracerebral Hemorrhage
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to test the hypothesis that minimally invasive surgery (MIS), an emerging surgical treatment for spontaneous intracerebral hemorrhage (sICH), is associated with better clinical outcomes than open craniotomy with clot evacuation, in a large, nationwide US cohort. METHODS: We performed a retrospective cohort study that included patients with sICH included in the American Heart Association Get With The Guidelines-Stroke registry between January 1, 2011, and December 31, 2021. We excluded patients with a diagnosis of ischemic stroke, subarachnoid hemorrhage, or subdural hemorrhage, and patients transferred to another hospital. The exposure was the type of surgery, classified as either open craniotomy with clot evacuation or MIS (composite of endoscopic evacuation or stereotactic evacuation with fibrinolytic therapy). The primary outcome was in-hospital mortality. Secondary outcomes at discharge included disposition, ambulatory status, and modified Rankin Scale (mRS) score. After overlap-weighted propensity score matching, multiple logistic regression was used to study the association between the type of surgery and outcomes. RESULTS: Among 564,265 patients with sICH, 7,770 underwent surgical intervention. MIS was performed in 703 patients and open craniotomy was performed in 7,067 patients. In regression analyses, MIS was associated with lower odds of in-hospital mortality (adjusted odds ratio [aOR] = 0.7, 95% confidence interval [CI] = 0.5-0.9), unfavorable discharge (aOR = 0.7, 95% CI = 0.6-0.9), and higher odds of discharge to rehabilitation (aOR = 1.3, 95% CI = 1.1-1.5), but not with functional outcomes. INTERPRETATION: In this large, representative US cohort of patients with sICH, MIS was associated with reduced in-hospital mortality and better discharge disposition compared to conventional open craniotomy with clot evacuation. ANN NEUROL 2026;99:871-880.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".