Understanding the neuroinflammatory response following concussion to develop treatment strategies
Bibliographic record
Abstract
Mild traumatic brain injuries (mTBI) have been associated with long-term cognitive deficits relating to trauma-induced neurodegeneration. These long-term deficits include impaired memory and attention, changes in executive function, emotional instability, and sensorimotor deficits. Furthermore, individuals with concussions show a high co-morbidity with a host of psychiatric illnesses (e.g., depression, anxiety, addiction) and dementia. The neurological damage seen in mTBI patients is the result of the impact forces and mechanical injury, followed by a delayed neuroimmune response that can last hours, days, and even months after the injury. As part of the neuroimmune response, a cascade of pro- and anti-inflammatory cytokines are released and can be detected at the site of injury as well as subcortical, and often contralateral, regions. It has been suggested that the delayed neuroinflammatory response to concussions is more damaging then the initial impact itself. However, evidence exists for favorable consequences of cytokine production following traumatic brain injuries as well. In some cases, treatments that reduce the inflammatory response will also hinder the brain's intrinsic repair mechanisms. At present, there is no evidence-based pharmacological treatment for concussions in humans. The ability to treat concussions with drug therapy requires an in-depth understanding of the pathophysiological and neuroinflammatory changes that accompany concussive injuries. The use of neurotrophic factors [e.g., nerve growth factor (NGF)] and anti-inflammatory agents as an adjunct for the management of post-concussion symptomology will be explored in this review.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".