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Record W2114295394 · doi:10.5770/cgj.v14i1.1

Blood Pressure in Relation to Age and Frailty

2011· article· en· W2114295394 on OpenAlexaffvenueabout
Michael R.H. Rockwood, Susan E. Howlett

Bibliographic record

VenueCanadian Geriatrics Journal · 2011
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineBlood pressureFrailty IndexCohortInternal medicinePopulationCohort studyGerontologyCardiology

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: On average, systolic blood pressure (SBP) rises with age, while diastolic blood pressure (DBP) increases to age 50 and then declines. As elevated blood pressure is associated with cardiovascular disease and mortality, it also might be linked to frailty. We assessed the association between blood pressure, age, and frailty in a representative population-based cohort. METHODS: Individuals from the second clinical examination of the Canadian Study of Health and Aging (n = 2305, all 70+ years) were separated into four groups: history of hypertension ± antihypertensive medication, and no history of hypertension ± antihypertensive medication. Frailty was quantified as deficits accumulated in a frailty index (FI). RESULTS: SBP and DBP changed little in relation to age, except in untreated hypertension, where SBP declined in individuals >85 years. In contrast, SBP declined in all groups up to an FI of 0.55, and then rose sharply. DBP changed little in relation to FI. The slope of the line relating FI and age was highest in untreated individuals without a history of hypertension, indicating the highest physiological reserve. CONCLUSIONS: SBP declined as frailty increased in older adults, except at the highest FI levels. SBP and age had little or no relationship.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.245
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations42
Published2011
Admission routes3
Has abstractyes

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