Effect of prior anticoagulation therapy on outcomes of traumatic brain injury: A systematic review and meta‑analysis
Bibliographic record
Abstract
Anticoagulants are commonly prescribed for multiple conditions. However, their influence on traumatic brain injury (TBI) outcomes, especially mortality, is not clear. The present study aimed to explore the effect of prior anticoagulation treatment on the outcomes of TBI. PubMed, Embase, Cochrane Central Register of Controlled Trials, Scopus and CINAHL databases were systematically searched for studies on individuals diagnosed with TBI, with a subgroup on prior anticoagulation therapy. Outcomes of interest included overall mortality, in-hospital mortality, length of hospital and intensive care unit stay, need for neurosurgical intervention and discharge rate. Cohort and case-control studies, published up to September 2023, were examined. Analysis was performed using STATA version 14.2 software and the Newcastle Ottawa Scale was used for bias assessment. A total of 22 studies (102,036 participants) were included in the analysis. Patients with TBI with prior anticoagulation treatment showed a statistically higher overall mortality risk [odds ratio (OR): 1.967, 95% confidence interval (CI): 1.481-2.613]. Subgroup analyses revealed age-specific and TBI severity-specific variations. Prior anticoagulation treatment was associated with a 1.860-times higher rate of in-hospital mortality and a significantly increased likelihood of requiring neurosurgical intervention (OR: 1.351, 95%CI: 1.068-1.708). However, no significant difference was noted in lengths of hospital or ICU stays. Patients with TBI and prior anticoagulation therapy are at higher risk of overall and in-hospital mortality and have significantly higher likelihood of needing neurosurgical interventions. The results emphasized the need for tailored therapeutic approach and more comprehensive clinical guidelines. Future investigations on specific anticoagulant types and immediate post-TBI interventions could offer further insights.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".