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The inter-arm systolic blood pressure difference and risk of cardiovascular mortality: A meta-analysis of cohort studies

2016· article· en· W6921031396 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBlood pressureHazard ratioCohort studyCohortDiseaseProportional hazards modelAtherosclerotic cardiovascular diseaseMean difference

Abstract

fetched live from OpenAlex

The inter-arm systolic blood pressure difference (SBPD) is recommended to be in relation to potential cardiovascular disease (CVD). Previous studies yielded controversial results about the association between an inter-arm SBPD ≥ 10 mmHg or ≥15 mmHg and the risk of cardiovascular mortality. Therefore, we conducted this meta-analysis to investigate this association. We searched PubMed and Embase databases through December 31, 2014, and examined the references of retrieved articles to identify relevant cohort studies. We utilized Newcastle–Ottawa scale to assess the quality of included studies and calculated the summary risk estimates in a fixed/random-effect model. All data analyses were conducted using STATA version 11.0. A total of seven studies were identified. Compared with participants with an inter-arm SBPD < 10 mmHg, the pooled hazard ratio (HR) of CVD mortality of those with an inter-arm SBPD ≥ 10 mmHg was 1.58 (95% CI: 1.3–1.93),and the pooled HR of cardiovascular mortality of participants with an inter-arm SBPD ≥ 15 mmHg versus those with an inter-arm SBPD < 15 mmHg was 1.88 (95% CI: 1.33–2.66). The findings from the present meta-analysis indicated that the detection of an inter-arm SBPD may define a subpopulation at high risk of CVD events.

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)0.019
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.035
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0120.057
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.145
GPT teacher head0.316
Teacher spread0.171 · 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 designMeta-analysis
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
Published2016
Admission routes1
Has abstractyes

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