Beta-blockers and Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To determine if beta-(β)-blockers improve outcomes after acute traumatic brain injury (TBI). BACKGROUND: There have been no new inpatient pharmacologic therapies to improve TBI outcomes in a half-century. Treatment of TBI patients with β-blockers offers a potentially beneficial approach. METHODS: Using MEDLINE, EMBASE, and CENTRAL databases, eligible articles for our systematic review and meta-analysis (PROSPERO CRD42016048547) included adult (age ≥ 16 years) blunt trauma patients admitted with TBI. The exposure of interest was β-blocker administration initiated during the hospitalization. Outcomes were mortality, functional measures, quality of life, cardiopulmonary morbidity (e.g., hypotension, bradycardia, bronchospasm, and/or congestive heart failure). Data were analyzed using a random-effects model, and represented by pooled odds ratio (OR) with 95% confidence intervals (CI) and statistical heterogeneity (I). RESULTS: Data were extracted from 9 included studies encompassing 2005 unique TBI patients with β-blocker treatment and 6240 unique controls. Exposure to β-blockers after TBI was associated with a reduction of in-hospital mortality (pooled OR 0.39, 95% CI: 0.27-0.56; I = 65%, P < 0.00001). None of the included studies examined functional outcome or quality of life measures, and cardiopulmonary adverse events were rarely reported. No clear evidence of reporting bias was identified. CONCLUSIONS: In adults with acute TBI, observational studies reveal a significant mortality advantage with β-blockers; however, quality of evidence is very low. We conditionally recommend the use of in-hospital β-blockers. However, we recommend further high-quality trials to answer questions about the mechanisms of action, effectiveness on subgroups, dose-response, length of therapy, functional outcome, and quality of life after β-blocker use for TBI.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".