Early vs. Late Tracheostomy in Patients with Traumatic Brain Injury: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction. Tracheostomy can help weaning in long-term ventilated patients, reducing the duration of mechanical ventilation and intensive care unit length of stay, and decreasing complications from prolonged tracheal intubation. In traumatic brain injury (TBI), ideal timing for tracheostomy is still debated. We performed a systematic review and meta-analysis to evaluate the effects of timing (early vs. late) of tracheostomy on mortality and incidence of VAP in traumatic brain-injured patients. Methods. This study was conducted in conformity with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. We performed a search in PubMed, using an association between heading terms: early, tracheostomy, TBI, prognosis, recovery, impact, mortality, morbidity, and brain trauma OR brain injury. Two reviewers independently assessed the methodological quality of eligible studies using the Newcastle–Ottawa Scale (NOS). Comparative analyses were made among Early Tracheostomy (ET) and late tracheostomy (LT) groups. Our primary outcome was the odds ratio of mortality and incidence of VAP between the ET and LT groups in acute brain injury patients. Secondary outcomes included the standardized mean difference (MD) of the duration of mechanical ventilation, ICU length of stay (LOS), and hospital LOS. Results. We included two randomized controlled trials, three observational trials, one cross-sectional study, and three retrospective cohort studies. The total number of participants in the ET group was 2509, while in the LT group it was 2597. Early tracheostomy reduced risk for incidence of pneumonia, ICU length of stay, hospital length of stay and duration of mechanical ventilation, but not mortality. Conclusions. In TBI patients, early tracheostomy compared with late tracheostomy might reduce risk for VAP, ICU and hospital LOS, and duration of mechanical ventilation, but increase the risk of mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.032 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".