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Record W2980711002 · doi:10.1016/j.jalz.2019.06.2672

P2‐265: THE CLINICAL CHARACTERISTICS OF COGNITIVE IMPAIRMENT IN PATIENTS WITH SMALL VESSEL DISEASE

2019· article· en· W2980711002 on OpenAlexaboutno aff
Junliang Yuan

Bibliographic record

VenueAlzheimer s & Dementia · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsStroop effectMontreal Cognitive AssessmentNeuropsychologyHyperintensityPsychologyAudiologyFrontal lobeInternal medicineCognitionTemporal lobeCognitive impairmentMedicineCardiologyMagnetic resonance imagingPsychiatryRadiology

Abstract

fetched live from OpenAlex

To explore the clinical characteristics of the cognitive disorders in patients with small vessel disease (SVD). A total of 60 patients with SVD and 50 age, gender-matched healthy subjects were recruited consecutively from the department of Neurology, Beijing Chaoyang Hospital. All the participants were performed by a battery of neuropsychological tests. The Fazekas scale was utilized to assess the severity of white matter lesions, and the scale of medial temporal lobe atrophy (MTA) was to evaluate the severity of medial temporal lobe. Patients with SVD were associated with global cognitive function deficits, including the general tests of MMSE (25.9±2.4 vs 28.1±1.7) and MoCA (23.0±3.7 vs 26.2±3.0), and also with performances of AVLD-I, AVLT-D, AVLT-R, TMT-B, Stroop B, Stroop C and DST (P<0.05). The score of MOCA was related negatively with Fazekas scale (r=−0.361, P=0.04). The severity of Fazekas had a positive relationship with the scores of MTA (r=0.449, P=0.032). Patients with SVD are closely correlated to general cognitive impairment, especially with memory decline, attention and executive function, which may be attributed to the impairment of frontal-subcortical circle.

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 score0.009
Threshold uncertainty score0.384

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 head0.039
GPT teacher head0.281
Teacher spread0.241 · 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
Published2019
Admission routes1
Has abstractyes

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