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Traumatic Brain Injury as a Risk Factor for Alzheimer's Disease: Is Inflammatory Signaling a Key Player?

2016· review· en· W2286414115 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Alzheimer Research · 2016
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsSt. Boniface Hospital
Fundersnot available
KeywordsTraumatic brain injuryDiseaseMedicineDementiaNeurodegenerationRisk factorChronic traumatic encephalopathyInflammationNeuroscienceBioinformaticsPsychiatryPoison controlPsychologyIntensive care medicineInjury preventionConcussionPathologyInternal medicineMedical emergency

Abstract

fetched live from OpenAlex

Traumatic brain injury (TBI) has become a significant medical and social concern within the last 30 years. TBI has acute devastating effects, and in many cases, seems to initiate long-term neurodegeneration. With advances in medical technology, many people are now surviving severe brain injuries and their long term consequences. Post trauma effects include communication problems, sensory deficits, emotional and behavioral problems, physical complications and pain, increased suicide risk, dementia, and an increased risk for chronic CNS diseases, such as Alzheimer's disease (AD). In this review, we provide an introduction to TBI and hypothesize how it may lead to neurodegenerative disease in general and AD in particular. In addition, we discuss the evidence that supports the hypothesis that TBI may lead to AD. In particular, we focus on inflammatory responses as key processes in TBI-induced secondary injury, with emphasis on nuclear factor kappa B (NF-κB) signaling.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.319
GPT teacher head0.489
Teacher spread0.170 · 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