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Diffusion tensor imaging and the Montreal cognitive assessment for assessing severe traumatic brain injury

2014· article· en· W3030622902 on OpenAlexaboutno aff
Xiaonian Zhang, Yajing Hou, Xinting Sun, Qianqian Chi, Hao Zhang

Bibliographic record

VenueZhonghua wuli yixue zazhi · 2014
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCorpus callosumFractional anisotropyDiffusion MRIMontreal Cognitive AssessmentSuperior longitudinal fasciculusWhite matterFasciculusPsychologyTraumatic brain injuryMedicineInternal capsuleAudiologyNeuroscienceCognitionMagnetic resonance imagingCognitive impairmentPsychiatryRadiology

Abstract

fetched live from OpenAlex

Objective To investigate any correlation between diffusion tensor imaging (DTI) results and Montreal cognitive assessment (MoCA) scores after severe traumatic brain injury (TBI).Methods Eight male pa-tients with chronic severe TBI were given the MoCA (including memory,attention,speech and executive function).DTI was used to quantify the fractional anisotropy (FA) of white matter fiber tracts in the radial and longitudinal fasciculus,under longitudinal fasciculus,internal capsule,corpus callosum genu and body,and the cingulate cortex.Pearson correlation coefficients were calculated to quantify the correlation between the FA values and MoCA scores.Results There was a positive correlation between FA in the corpus callosum body,corpus callosum genu and the superior longitudinal fasciculus and MoCA total scores.Conclusion The MoCA scores of patients with chronic severe TBI are related to white matter damage in the corpus callosum body,corpus callosum genu and the superior longitudinal fasciculus. Key words: Brain injury;  Cognitive impairment;  Montreal cognitive assessment;  Diffusion tensor imaging ;  Rehabilitation

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.929
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.376
Teacher spread0.339 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2014
Admission routes1
Has abstractyes

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