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Record W2416834318 · doi:10.1097/wnr.0000000000000443

Cognitive impairment and gray matter volume abnormalities in silent cerebral infarction

2015· article· en· W2416834318 on OpenAlexaboutno aff
Tao Yang, Lan Zhang, Mingqing Xiang, Wei Luo, Jinbai Huang, Maokun Li, Hua Wang

Bibliographic record

VenueNeuroreport · 2015
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersHealth and Family Planning Commission of Hubei Province
KeywordsParahippocampal gyrusTemporal lobeMedial frontal gyrusFrontal lobeInferior temporal gyrusCardiologyMontreal Cognitive AssessmentPsychologyAudiologyMedicineCognitionInternal medicineCerebral infarctionNeuroscienceCognitive impairmentIschemiaEpilepsy

Abstract

fetched live from OpenAlex

To investigate the association between cognitive impairment and gray matter volume (GMV) abnormalities in silent cerebral infarction (SCI) patients, the GMV of 62 pairs of patients and well-matched healthy controls was calculated. All participants underwent a P300 test, a Montreal Cognitive Assessment (MoCA) test. Compared with controls, the patients showed decreased GMV in the left superior frontal gyrus, left inferior frontal gyrus, left superior temporal gyrus, right middle temporal gyrus, and bilateral parahippocampal gyrus; no significantly increasing GMV was found. The volumes of the frontal and temporal lobes were positively correlated with the score of the MoCA scale and P300 amplitudes (r≥0.62, P<0.01). The P300 latency was negatively correlated with the volumes of the frontal lobe, the temporal lobe, and the hippocampus (r≤-0.71, P<0.05). No significant correlations between the GMV of the abnormal brain regions and four clinical characteristics in SCI patients were found, suggesting that cognitive deficiency existed in SCI patients and the reduced GMV might contribute to the pathology of cognitive deficiency in SCI patients.

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 score0.015
Threshold uncertainty score0.560

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)0.0010.000

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 head0.027
GPT teacher head0.308
Teacher spread0.282 · 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

Citations13
Published2015
Admission routes1
Has abstractyes

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