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Record W2319949533 · doi:10.1089/neu.2014.3506

Aging, Neurodegenerative Disease, and Traumatic Brain Injury: The Role of Neuroimaging

2014· review· en· W2319949533 on OpenAlexafffund
Carrie Esopenko, Brian Levine

Bibliographic record

VenueJournal of Neurotrauma · 2014
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoBaycrest Hospital
FundersCanadian Institutes of Health ResearchOntario Neurotrauma FoundationAlzheimer's Society
KeywordsChronic traumatic encephalopathyTraumatic brain injuryNeuroimagingMedicineNeurodegenerationContext (archaeology)DiseaseNeuroscienceConcussionPsychologyPoison controlPsychiatryInjury preventionPathology

Abstract

fetched live from OpenAlex

Traumatic brain injury (TBI) is a highly prevalent condition with significant effects on cognition and behavior. While the acute and sub-acute effects of TBI recover over time, relatively little is known about the long-term effects of TBI in relation to neurodegenerative disease. This issue has recently garnered a great deal of attention due to publicity surrounding chronic traumatic encephalopathy (CTE) in professional athletes, although CTE is but one of several neurodegenerative disorders associated with a history of TBI. Here, we review the literative on neurodegenerative disorders linked to remote TBI. We also review the evidence for neuroimaging changes associated with unhealthy brain aging in the context of remote TBI. We conclude that neuroimaging biomarkers have significant potential to increase understanding of the mechanisms of unhealthy brain aging and neurodegeneration following TBI, with potential for identifying those at risk for unhealthy brain aging prior to the clinical manifestation of neurodegenerative disease.

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 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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.173
GPT teacher head0.431
Teacher spread0.258 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations61
Published2014
Admission routes2
Has abstractyes

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