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Record W2943987591 · doi:10.25011/cim.v42i1.32391

Blood pressure measurement and the prevalence of postprandial hypotension

2019· article· en· W2943987591 on OpenAlexaffvenue
Kenneth Madden, Boris Feldman, Graydon S. Meneilly

Bibliographic record

VenueClinical and investigative medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Syncope and Autonomic Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPostprandialMedicineBlood pressureMealInternal medicineMean blood pressureCardiologyPopulationHeart rate

Abstract

fetched live from OpenAlex

BACKGROUND: Postprandial hypotension (PPH) is a serious condition that has been shown to be an independent risk factor for falls, fractures and death. PURPOSE: The prevalence of this problem in older adults with a past history of falls has shown a wide variability in the literature; the present study seeks to examine how the frequency with which blood pressure is measured impacts the prevalence and severity of PPH. METHODS: Older adults were recruited sequentially from a geriatric medicine falls clinic for meal testing (n=95). All subjects (mean age 77.5±0.7 years, 61±5% female) were fasting prior to each 90 min standardized meal test. A Finometer (Finapres Medical Systems BV) was used to monitor blood pressure. Beat-by-beat systolic (SBP) measures were averaged for 0.5, 1, 2, 3, 5, 6, 9, 10, 15, 18, 30, 45 and 90 min respectively during the meal test. RESULTS: Using the original diagnostic method of checking mean blood pressure every 10 min resulted in a PPH prevalence of 42.1±5.1% in our population, with an overall range from 81.1±4.0% to 11.6±3.3% depending on the frequency of calculating SBP. The maximal observed postprandial decrease in SBP also showed a significant difference with blood pressure measurement frequency (p.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.007
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 head0.084
GPT teacher head0.288
Teacher spread0.203 · 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".

Quick stats

Citations5
Published2019
Admission routes2
Has abstractyes

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