Comparing surgical outcomes: Craniotomy versus decompressive craniectomy in acute subdural hematoma - A systematic review and meta-analysis
Bibliographic record
Abstract
Acute subdural hematomas (SDH) pose a significant health risk, often resulting from traumatic head injuries. The choice between surgical interventions, craniotomy, and decompressive craniectomy, remains a subject of debate. This meta-analysis aims to compare outcomes and guide clinical decision-making. Following PRISMA guidelines, a comprehensive literature search was conducted in databases such as Ovid Medline, PubMed, and Cochrane, up to December 2023. Selection criteria included studies comparing craniotomy and decompressive craniectomy for acute SDH. Data extraction utilized the Newcastle–Ottawa Quality Assessment Tool, and statistical analysis employed the random-effects model. The meta-analysis included 17 studies and 6848 patients. Craniotomy demonstrated a significant reduction in mortality rates (RR 0.80, 95% CI 0.73–0.89, P < 0.0001). GCS scores favored craniotomy for severe cases. GOS outcomes showed a trend favoring craniotomy, particularly in good recovery (RR 1.34, 95% CI 1.04–1.74, P = 0.03). Additional factors explored included co-existing sub-epidural hematoma, mydriasis, extracranial injuries, residual SDH, revision rates, and intracranial pressure. The meta-analysis suggests that craniotomy may be a favorable surgical strategy for acute SDH, displaying a significant decrease in mortality rates and a lower risk of raised intracranial pressure. However, the nuanced nature of outcomes emphasizes the need for a tailored approach, considering broader clinical contexts. Future research should address limitations and provide a basis for well-informed clinical decision-making.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.053 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".