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Record W7011377614

Mild Traumatic Brain Injury and Neuroinflammation

2023· dissertation· en· W7011377614 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
FundersMcMaster University
KeywordsTraumatic brain injuryNeuroinflammationContext (archaeology)ConcussionPost-concussion syndromeHead injuryPathophysiology
DOInot available

Abstract

fetched live from OpenAlex

Despite being a common problem, there are many gaps in the understanding of mild traumatic brain injury (mTBI). Its pathophysiology is unclear, diagnostic criteria are variable, and the associated symptomatology is non-specific. As a result, there are challenges associated with precise mTBI diagnosis and treatment. The dissertation seeks to identify distinctive features, both clinical and pathophysiological, exclusively associated with mTBI. In addition, the neuroinflammatory component of mTBI is explored in detail in the context of inflammatory cytokines’ potential use as prognostic biomarkers and development of a targeted treatment. Three studies were conducted to explore mTBI. We conducted a retrospective chart review to identify the clinical presentation exclusively associated with mTBI that sets it apart from other similar conditions. This was accomplished through symptomatology comparison between the patients with head injuries that meet the ACRM (1993) criteria for mTBI diagnosis vs. those who do not. The results of this study showed that 20.5% of patients with chronic post-concussive symptoms do not meet the ACRM (1993) criteria of mTBI despite sustaining a head injury. In addition, no symptom specific differences were found between the two populations. A detailed systematic review and meta-analysis were also conducted to identify the common inflammatory cytokines associated with mTBI and to explore their potential use as prognostic biomarkers. The results show significantly elevated blood IL-6, IL-1RA, IFN-γ (at <24 hrs.) and MCP-1/CCL2 (within a week) levels in patients with mTBI compared to healthy controls in majority of the included studies. A meta-analysis was further conducted that supported these findings by showing significantly elevated IL-6, MCP-1/CCL2, and IL-1β levels in patients with mTBI in the acute stages (<7 days). In addition, elevated IL-6, TNF-α, IL-1RA, IL-10, and MCP-1/CCL2 levels were associated with poor prognosis in patients with mTBI. In addition, a systematic review was conducted to identify the inflammatory cytokines associated with adverse psychological outcome in population with mTBI. The results show that IL-6, TNF-α, IL-10, and CRP are associated with PTSD and/or depression in the population with mTBI, particularly in the chronic stages. Collectively, these studies show that all symptomatic patients with head trauma, whether or not they meet the subjective criteria of mTBI, should be managed and offered early rehabilitation to avoid long tern adverse consequences. In addition, this thesis supports the neuro-inflammatory hypothesis of mTBI and identifies inflammatory cytokines that could be potentially utilized as prognostic biomarkers and for the development of mTBI treatment.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.029
GPT teacher head0.252
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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