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Record W2358339361

Influence of cerebral white matter lesions on cognitive function

2012· article· en· W2358339361 on OpenAlexaboutno aff
Ganqin Du

Bibliographic record

VenueJournal of Zhengzhou University · 2012
Typearticle
Languageen
FieldNeuroscience
TopicBrain Tumor Detection and Classification
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentCognitionHyperintensityCognitive impairmentMedicineWhite matterLesionAudiologyOrientation (vector space)Visual memoryPsychologyMagnetic resonance imagingPathologyRadiologyPsychiatryGeometry
DOInot available

Abstract

fetched live from OpenAlex

Aim:To investigate the cognitive function impairment of patients with cerebral white matter lesion(WML) at different extent and different location.Methods:According to MRI T2 weighted-imaging and FLAIR imaging,75 patients were first classified into 3 subgroups(mild,moderate,and severe) depending on the extent,then were classified into 2 subgroups(periventricular lesions and deep WML) depending on the location.Thirty normal people were regarded as control group.Cognitive performance was assessed using the Montreal Cognitive Assessment(MoCA).Results:Compared with control,WML group had significantly low scores in total MoCA score,visual spatial,executive function,language,memory and orientation.Moderate and severe subgroups had significantly low scores in attention,calculation and abstraction;severe subgroup had significantly low scores in naming(P 0.05).The periventricular lesion subgroup had significantly low scores in total MoCA score,calculation,language,abstraction,memory and orientation,and insignificantly high scores in visual spatial,executive function or attention compared with deep WML subgroup.Conclusion:Patients with WML undergo cognitive function impairment,especially in visual spatial,executive function,language,abstraction,memory and orientation.Periventricular and deep WML may have different influence on cognitive function.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.239

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 communication0.0000.001
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 head0.030
GPT teacher head0.234
Teacher spread0.204 · 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 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
Published2012
Admission routes1
Has abstractyes

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