Effects of Decompressive Craniectomy on Functional Outcomes and Mortality in Poor-Grade Aneurysmal Subarachnoid Hemorrhage
Bibliographic record
Abstract
Background: Decompressive craniectomy (DC) is often considered as a life-saving measure for trauma and ischemic stroke. Selected patients with poor-grade aneurysmal subarachnoid hemorrhage (aSAH) have been subjected to DC, but its benefits remain unknown. The aim of this meta-analytic review is to surmise the overall effects of DC on poor-grade aSAH outcomes. Methods: Data were acquired from previous publications on DC and the Subarachnoid Hemorrhage International Trialists (SAHIT) Repository. We performed both study-level and individual participant analysis. Results: Fifteen published studies and one dataset from SAHIT met our inclusion criteria. DC was associated with high event rate of unfavorable outcomes and mortality, however, the overall quality of evidence was poor due to high risks of bias and significant heterogeneity in most outcome measures. Conclusion: The currently available evidence suggests that the impact of DC on poor-grade aSAH outcomes is trending toward a harmful effect. High-quality prospective studies are urgently needed.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".