Inflammatory cytokines associated with mild traumatic brain injury and clinical outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
Mild traumatic brain injuries (mTBIs) trigger a neuroinflammatory response, which leads to perturbations in the levels of inflammatory cytokines, resulting in a distinctive profile. A systematic review and meta-analysis were conducted to synthesize data related to levels of inflammatory cytokines in patients with mTBI. The electronic databases EMBASE, MEDLINE, and PUBMED were searched from January 2014 to December 12, 2021. A total of 5,138 articles were screened using a systematic approach based on the PRISMA and R-AMSTAR guidelines. Of these articles, 174 were selected for full-text review and 26 were included in the final analysis. The results of this study demonstrate that within 24 hours, patients with mTBI have significantly higher levels of Interleukin-6 (IL-6), Interleukin-1 Receptor Antagonist (IL-1RA), and Interferon-γ (IFN-γ) in blood, compared to healthy controls in majority of the included studies. Similarly one week following the injury, patients with mTBI have higher circulatory levels of Monocyte Chemoattractant Protein-1/C-C Motif Chemokine Ligand 2 (MCP-1/CCL2), compared to healthy controls in majority of the included studies. The results of the meta-analysis also confirmed these findings by demonstrating significantly elevated blood levels of IL-6, MCP-1/CCL2, and Interleukin-1 beta (IL-1β) in the mTBI population compared to healthy controls ( p < 0.0001), particularly in the acute stages (<7 days). Furthermore, it was found that IL-6, Tumor Necrosis Factor-alpha (TNF-α), IL-1RA, IL-10, and MCP-1/CCL2 were associated with poor clinical outcomes following the mTBI. Finally, this research highlights the lack of consensus in the methodology of mTBI studies that measure inflammatory cytokines in the blood, and also provides direction for future mTBI research.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".