Melatonin as a Treatment after Traumatic Brain Injury: A Systematic Review and Meta-Analysis of the Pre-Clinical and Clinical Literature
Bibliographic record
Abstract
Traumatic brain injury (TBI) is common; however, effective treatments of the secondary brain injury are scarce. Melatonin is a potent, nonselective neuroprotective and anti-inflammatory agent that is showing promising results in neonatal brain injury. The aim of this study was to systematically evaluate the pre-clinical and clinical literature on the effectiveness of melatonin in improving outcome after TBI. Using the systematic review protocol for animal intervention studies (SYRCLE) and Cochrane methodology for clinical studies, a search of English-language articles was performed. Eligible studies were identified and data were extracted. Quality assessment was performed using the SYRCLE risk of bias tool. Meta-analyses were performed using standardized mean differences (SMD). Seventeen studies (15 pre-clinical, 2 clinical) met inclusion criteria. There was heterogeneity in the studies, and all had moderate-to-low risk of bias. Meta-analysis of pre-clinical data revealed an overall positive effect on neurobehavioural outcome with SMD of 1.51 (95% CI: 1.06-1.96). Melatonin treatment had a favorable effect on neurological status, by an SMD of 1.35 (95% CI: 0.83-1.88), and on cognition by an SMD of 1.16 (95% CI: 0.4-1.92). Melatonin decreased the size of the contusion by an SMD of 2.22 (95% CI: 0.8--3.59) and of cerebral edema by an SMD of 1.91 (95% CI: 1.08-2.74). Only two clinical studies were identified. They were of low quality, were used for symptom management, and were of uncertain significance. In conclusion, there is evidence that melatonin treatment after TBI significantly improves both behavioral outcomes and pathological outcomes; however, significant research gaps exist, especially in clinical populations.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".