The Impact of Intervention Modality on Mortality Outcomes in Patients With Hemorrhagic Strokes: A Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Hemorrhagic strokes significantly contribute to stroke-related mortality. Despite advancements in critical care and neuroimaging, optimal management strategies for these strokes remain debated. Medical management focuses on stabilizing physiological parameters and preventing hematoma expansion, whereas surgical evacuation directly addresses mass effect and lowers intracranial pressure (ICP). This meta-analysis evaluates the impact of various intervention modalities and compares surgical versus medical interventions on mortality outcomes in patients with hemorrhagic strokes. MATERIALS AND METHODS: journals. Quality assessment was performed using the Newcastle-Ottawa scale. Randomized and nonrandomized controlled trials, case series and reports, and prospective, observational, and retrospective studies reporting mortality outcomes in patients treated with medical and/or surgical interventions were included. The primary outcome assessed was overall mortality. Odds ratios (ORs) with 95% confidence intervals (CIs) were pooled using a random-effects model. Statistical heterogeneity was evaluated using the Q test and I² statistic, while publication bias was assessed using Duval and Tweedie's Trim and Fill method. RESULTS: Seventeen studies, encompassing 9077 patients, were included, with nine evaluating surgical management and eight examining medical management. Pooled analysis showed a modest but statistically significant reduction in mortality across all intervention groups compared with control (p = 0.05; OR: 1.218; 95% CI: 1.000-1.483). Subgroup analysis indicated that surgical interventions significantly decreased mortality compared with medical management (p = 0.009; OR: 1.273; 95% CI: 1.057-1.489), suggesting a survival benefit. In contrast, medical management did not achieve statistical significance (p = 0.606). Moderate heterogeneity was noted (I² = 51.997%), and sensitivity analysis confirmed the robustness of the findings. DISCUSSION: This study shows that surgical and medical management strategies enhance survival in patients with hemorrhagic stroke, with surgical interventions showing a notable mortality advantage, particularly in cases of large hematomas or rapid neurological decline. Advanced surgical methods such as decompressive craniectomy and thrombolysis-assisted evacuation help reduce ICP and secondary brain injury. While medical therapies aim to prevent hematoma expansion, their impact on mortality has been limited. These findings may encourage clinicians to adopt surgical interventions in high-risk patients to optimize outcomes. Moreover, medical management strategies need reevaluation due to their limited effect on mortality. Future research should focus on the timing of interventions and enhancing functional recovery through multidisciplinary rehabilitation. CONCLUSION: This study highlights that both surgical and medical management are critical to improving outcomes in hemorrhagic strokes. Surgical interventions, guided by hematoma characteristics, provide a survival advantage in selected cases. However, individualized treatment planning remains essential, emphasizing the need for optimized medical care, including intensive blood pressure control and comprehensive supportive management.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.050 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".