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Within‐Visit Variability of Blood Pressure and All‐Cause and Cardiovascular Mortality Among US Adults

2012· article· en· W1562729366 on OpenAlexaff
Paul Muntner, Emily B. Levitan, Kristi Reynolds, David Mann, Marcello Tonelli, Suzanne Oparil, Daichi Shimbo

Bibliographic record

VenueJournal of Clinical Hypertension · 2012
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineHazard ratioBlood pressureConfidence intervalInternal medicineStandard deviationDemographyDiastoleCardiologyStatistics

Abstract

fetched live from OpenAlex

The association between within-visit variability of systolic blood pressure (SBP) and diastolic blood pressure (DBP) and all-cause and cardiovascular (CVD) mortality was examined using the Third National Health and Nutrition Survey (n=15,317). Three SBP and DBP readings were taken by physicians during a single medical evaluation. Within-visit variability for each participant was defined using the standard deviation of SBP and DBP across these measurements. Mortality was assessed over 14 years (n=3848 and n=1684 deaths from all causes and CVD, respectively). After age, sex, and race-ethnicity adjustment, the hazard ratios (95% confidence intervals) for all-cause mortality associated with the 4 highest quintiles of within-visit standard deviation of SBP (2.00-2.99 mm Hg, 3.00-3.99 mm Hg, 4.00-5.29 mm Hg, and ≥5.30 mm Hg) compared with participants in the lowest quintile of within-visit standard deviation of SBP (<2.0 mm Hg) were 1.04 (0.87-1.26), 1.09 (0.92-1.29), 1.06 (0.88-1.28), and 1.13 (0.95-1.33), respectively (P=.136). The analogous hazard ratios for CVD mortality were 0.95 (0.69-1.32), 0.96 (0.67-1.36), 0.95 (0.74-1.23), and 1.04 (0.80-1.35), respectively (P=.566). No association with mortality was present after further adjustment and when modeling within-visit standard deviation of SBP as a continuous variable. Standard deviation of DBP was not associated with mortality.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.059
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.347
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

Citations38
Published2012
Admission routes1
Has abstractyes

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