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Relationship Between Postprandial Hypotension and Mild Cognitive Impairment in elderly patients

2017· article· en· W3029667175 on OpenAlexaboutno aff
Juan Jiang, Yanan Wei, Lihua Deng, Yuanyuan Chen, Hui Bao, Jie Liu, Jingtong Wang

Bibliographic record

VenueChin J Heart & Heart Rhythm(Electronic Edition) · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPostprandialMedicineMontreal Cognitive AssessmentIncidence (geometry)Logistic regressionCognitive impairmentInternal medicineCognitionAmbulatoryRisk factorGastroenterologyPsychiatry

Abstract

fetched live from OpenAlex

Objectives To explore the relationship between postprandial hypotension (PPH) and mild cognitive impariment(MCI) in elderly patients. Methods 296 senile inpatients with mean age of 78.95 years (range from 60 to 95 years) were recruited. 194 patients were male (65.5%). Their postprandial blood pressures were measured every 30 minutes, and then divided into 2 groups according to the definition of PPH: PPH group and non-PPH group. Cognitive function was assessed by Peking Union Medical College Hospital vision of Montreal Cognitive Assessment (MoCA-P), then divided into 2 groups according to the definition of MCI: MCI group and normal cognition (NC) group. Results There are 146 patients met PPH criteria (49.3%). 125 patients had MCI (42.2%). Compared with the non-PPH group, the PPH group had lower MoCA score (24.76±3.96 vs 25.66±2.98, P=0.028). The incidence of MCI in PPH group was significantly higer than in non-PPH group (50.7% vs 34%, P=0.005). Logistic regression analysis showed that age, average education years and PPH independently correlated with MCI [OR(95%CI) was 1.06(1.02-1.11), 0.83(0.76-0.90), 1.98(1.16-3.36) respectively, all P<0.05]. Conclusion PPH may be an independent risk factor for MCI in elderly patients. Key words: Postprandial hypotension; Mild cognitive impariment; Ambulatory blood pressure

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)0.000
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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.273
Teacher spread0.258 · 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
Published2017
Admission routes1
Has abstractyes

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