Gut Instinct: How the Microbiome Affects Traumatic Brain Injury, A Narrative Review
Bibliographic record
Abstract
Objectives: Emerging evidence suggests that the gut microbiome may play a role in the pathophysiology of traumatic brain injury (TBI). The objective of this systematic review is to identify and evaluate studies that investigate the relationship between TBI and gut microbiota alterations. Methods: Using the PRISMA 2020 Checklist, we searched five databases to identify relevant studies. Two independent researchers screened titles and abstracts and identified eligible studies according to the following PICO: studies that investigated the relationship between TBI and gut microbiota AND reported outcomes related to gut microbiome alterations. We assessed the risk of bias for included studies, extracted methodological data and related results of the articles, and used them for qualitative analysis. Results: We screened the titles and abstracts of 23 identified records and assessed the full text of 10 studies. In total, 5 studies met eligibility criteria and were entered into the qualitative analysis. These studies investigated the effects of TBI on gut microbiota in animal models and human patients. Although, we planned to systematic review, lack of adequate quantitively and qualitative data compelled us to write a narrative survey. The majority of studies reported significant alterations in gut microbiota composition and function following TBI, with potential implications for immune function, inflammation, and neurological recovery. Conclusion: This systematic review provides evidence supporting a relationship between TBI and alterations in gut microbiota. While the exact mechanisms underlying this relationship remain unclear, these findings suggest that targeting the gut microbiome may represent a novel therapeutic approach for TBI. Further studies are needed to elucidate the mechanisms involved and to evaluate the potential benefits of gut microbiota-targeted interventions in TBI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".