The role of decompressive craniectomy in traumatic brain injury: A systematic review and meta-analysis
Bibliographic record
Abstract
The objective is to evaluate the efficacy of early decompressive craniectomy (DC) versus standard medical management ± late DC in improving clinical outcome in patients with traumatic brain injury (TBI). Electronic databases and gray literature (unpublished articles) were searched under different MeSH terms from 1990 to present. Randomized control trials, case–control studies, and prospective cohort studies on DC in moderate and severe TBI. Clinical outcome measures included Glasgow Coma Outcome Scale (GCOS) and extended GCOS, and mortality. Data were extracted to Review Manager software. A total of 45 articles and abstracts that met the inclusion criteria were retrieved and analyzed. Ultimately, seven studies were included in our meta-analysis, which revealed that patients who had early DC had no statistically significant likelihood of having a favorable outcome at 6 months than those who had a standard medical care alone or with late DC (OR of favorable clinical outcome at 6 months: 1.00; 95% confidence interval (CI): 0.75–1.34; P = 0.99). The relative risk (RR) of mortality in early DC versus the standard medical care ± late DC at discharge or 6 months is 0.62; 95% CI: 0.40–0.94; P = 0.03. Subgroup analysis based on RR of mortality shows that the rate of mortality is reduced significantly in the early DC group as compared to the late DC. RR of Mortality is 0.43; 95% CI: 0.26–0.71; P = 0.0009. However, good clinical outcome is the same. Early DC saves lives in patients with TBI. However, further clinical trials are required to prove if early DC improve clinical outcome and to define the best early time frame in performing early DC in TBI population.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".