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Record W3201889129 · doi:10.1111/sdi.13017

Correlation between home systolic blood pressure variability and cognitive impairment in maintenance hemodialysis patients

2021· article· en· W3201889129 on OpenAlexaboutno aff
Huihui Xiao, Hua Li, Liping Dong, Xiaohong Song, Yang Wu, Honglan Wei, Weifeng Shang, Ming Tian, Junwu Dong

Bibliographic record

VenueSeminars in Dialysis · 2021
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsnot available
FundersHealth and Family Planning Commission of Wuhan Municipality
KeywordsMedicineBlood pressureLogistic regressionHemodialysisInternal medicineCognitionCorrelationCognitive impairmentCardiologyPhysical therapy

Abstract

fetched live from OpenAlex

INTRODUCTION: To investigate the correlation between home blood pressure variability and cognitive function in maintenance hemodialysis (MHD) patients. METHODS: Patients who received MHD were included. Their home blood pressure on nondialysis days within 1 week was collected. All patients were assessed with the Montreal Cognitive Assessment scale, according to which the patients were divided into cognitive impairment (CI) group and non-CI group, and the differences between two groups were compared. RESULTS: A total of 224 patients were included in the study, of which 168 had CI (75%). Compared with non-CI group, patients in CI group had larger variability of systolic blood pressure (SBPV) (8.4 [6.7, 10.6]% vs. 6.9 [4.9, 8.8]%, P < 0.001). The smooth fitting curve (OR = 1.2, 95% CI [1.1-1.4], P < 0.001) and trend test (P for trend = 0.004) showed that the risk of CI raised with the increase of SBPV. The patients were further divided into tertiles according to the SBPV. We also found a gradual increase in the proportion of incident CI in the three tertiles. Multiple logistic regression analysis showed that age, shorter years of education, less frequency of hemodialysis, and greater SBPV were the dependent risk of CI. CONCLUSION: In conclusion, greater SBPV indicates higher risk of cognitive impairment in MHD 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)0.001
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.231
Teacher spread0.226 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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