Minimally Invasive Surgery for Spontaneous Intracerebral Hemorrhage: Meta‐Analysis of High‐Quality Randomized Clinical Trials
Bibliographic record
Abstract
OBJECTIVES: Spontaneous intracerebral hemorrhage (ICH) poses high mortality and morbidity rates with limited evidence-based therapeutic approaches. We aimed to evaluate the current evidence for the role of minimally invasive surgery (MIS) in the management of ICH. METHODS: This systematic review and meta-analysis followed recommended guidelines and protocols. Medline, Embase, Scopus, and the Cochrane Library were searched from inception up to April 12, 2024. The inclusion was restricted to randomized clinical trials (RCTs) of high quality, ensuring they were not deemed to have a high risk of bias in any of the Cochrane risk of bias tool (RoB2) domains. Primary outcomes were good functional outcome (modified Rankin scale, 0-3) and mortality beyond 90 days. Secondary outcomes were early mortality within 30 days and rebleeding rates. We pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) using random-effects models. RESULTS: Fourteen high-quality RCTs were included. There were 3,027 patients with ICH (1,475 randomized to MIS, and 1,452 randomized to medical management or craniotomy). Of included patients, 1,899 (62.7%) were males. MIS resulted in higher odds of achieving long-term good functional outcome (OR, 1.51 [95% CI, 1.25-1.82]), lower odds of long-term mortality (OR, 0.72 [95% CI, 0.57-0.90]) and lower odds of early mortality (OR, 0.73 [95% CI, 0.56-0.95]). Rebleeding rates were similar (OR, 1.10 [95% CI, 0.55-2.19]). The treatment effect of MIS was consistent across multiple sensitivity and subgroup analyses, including individuals with deep ICH. INTERPRETATION: This meta-analysis provides high-quality clinical trial evidence supporting the use of MIS as a primary treatment strategy in the management of ICH. ANN NEUROL 2024.
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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 | 0.028 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.054 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".