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Capacity of cognitive control in patients with cerebral small vessel disease

2020· article· en· W3030570505 on OpenAlexaboutno aff
Zhiqi Wang, Jun Zhang, Qiang Wei, Shanshan Cao, Wen Pan, Kai Wang

Bibliographic record

VenueZhonghua xingwei yixue yu naokexue zazhi · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsStroop effectVerbal fluency testNeuropsychologyTrail Making TestInternal medicineAudiologyMedicineBoston Naming TestCognitionPaced Auditory Serial Addition TestMemory spanPsychologyCardiologyPsychiatryWorking memory

Abstract

fetched live from OpenAlex

Objective To evaluate the capacity of cognitive control(CCC) in patients with cerebral small vessel disease(CSVD) and explore the relationship between CCC and cognitive function in CSVD, and to assess the predict value of CCC on the occurrence of CSVD. Methods Twenty-two patients with CSVD and twenty-three healthy controls were enrolled.All of them completed the majority function task-masked (MFT-M) and a set of neuropsychological tests.Neuropsychological test was performed by Montreal cognitive assessment (MoCA), verbal fluency test (VFT), Chinese auditory learning test (CAVLT), symbol digit modalities test (SDMT), digital span (DS), Stroop color word test (SCWT), color trail test (CTT) and Modified Boston naming test (Modified BNT). The predict value of CCC for the occurrence of CSVD was assessed with logistic regression analysis. Results CCC of patients with CSVD was lower than that of healthy control ((2.97±0.72)bps vs (3.53±0.62)bps, t=-2.704, P=0.01). Between patients with CSVD and healthy control, there were significant differences in MoCA ((22.24±4.58 vs (24.86±2.42), t=-2.334, P=0.026), VFT-animal (12(6) vs 15(6), Z=-2.965, P=0.003), VFT-vegetables and fruits ((13.79±3.81) vs (18.27±4.13), t=-3.592, P=0.001), CAVLT-immediate ((7.45±2.18) vs (9.11±2.08), t=-2.502, P=0.017), CAVLT-short term delay ((7.20±3.32) vs (10.76±3.08), t=-3.564, P=0.001), CAVLT-long term delay ((7.30±3.16) vs (10.29±3.18), t=-3.012, P=0.005), SDMT ((15.95±5.49) vs (23.41±12.73), t=-2.513, P=0.018), CTT-A (85.17(42.60) vs 55.50(52.65), Z=-2.965, P=0.003), CTT-B ((200.69±71.35) vs (132.44±53.66), t=3.556, P=0.001), and CTT-B-A ((104.13±53.31) vs (65.20±35.98), t=2.819, P=0.007). But there was no significant difference in VFT-word begin with Chinese characterwater((3.68±2.63) vs (5.44±2.71), t=-1.940, P=0.061), CAVLT-recognition (14(3) vs 14(4), Z=-0.524, P=0.601), DS-forward (7.0(3.0) vs 5.5(2.0), Z=-0.152, P=0.880), DS-backward (4(1) vs 4(2), Z=-1.044, P=0.297), SCWT ((9.50±9.28) vs (5.94±10.47), t=1.123, P=0.268), Modified BNT (14.0(3.0) vs 13.5(3.0), Z=-0.727, P=0.467) between CSVD patients and healthy controls.In patients with CSVD, CCC was positively correlated with scores of MoCA (r=0.551, P=0.010) and also with DS-forward (r=0.532, P=0.013) and SCWT (r=-0.487, P=0.040). Logistic regression analysis showed that CCC was an important variable in predicting the possibility of CSVD (B=-1.318, P=0.019, OR=0.268, 95%CI (0.089-0.808)). Conclusion Compared with the healthy control, CCC in patients with CSVD decreases significantly and CCC is related to the cognitive impairment.CCC can predict the possibility of CSVD. Key words: Cognitive control capacity; Cerebral small vessel disease; 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.030
GPT teacher head0.209
Teacher spread0.180 · 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 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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