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Record W2003730214 · doi:10.1007/bf02895168

Anger expression, gender, and ambulatory blood pressure in mild, unmedicated adults with hypertension

2000· article· en· W2003730214 on OpenAlexaff
Karin F. Helmers, Brian Baker, Brian O’Kelly, Sheldon W. Tobe

Bibliographic record

VenueAnnals of Behavioral Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsYork University
Fundersnot available
KeywordsAngerAmbulatory blood pressureBlood pressureAmbulatoryMedicineHealth psychologyClinical psychologyInternal medicinePsychologyPsychiatryPublic healthPathology

Abstract

fetched live from OpenAlex

The suppression of anger has been associated with the development of hypertension. This study evaluated the association between anger management style (anger-in and anger-out) and ambulatory blood pressure (ABP) in patients with repeated clinic diastolic blood pressures (DBPs) between 90-105 mmHg, unmedicated and with no known coronary artery disease. A total of 128 men (46.0 years) and 66 women (46.6 years) participated. Fourteen percent of men and 35% of women were classified as having "white coat" hypertension (daytime DBP < 85 mmHg). Mean awake and sleep DBP and systolic blood pressure (SBP) were evaluated in a repeated measures analysis of variance (ANOVA). Anger-in and anger-out scores were categorized into low, medium, and high t-scores (< 50, 50-59, > or = 60). Results indicated that in women, increasing anger-in is associated with greater SBPs while awake and sleeping, whereas no effect was found for DBP, nor any effect in men. No significant association was found between gender, anger-out, and ABP. The clinical diagnostic status of white coat hypertension was not differentially associated with anger-in or anger-out in men and women. In conclusion, in a sample of mild unmedicated adults with hypertension, suppression of anger is associated with greater ambulatory SBP in women, but not in men.

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)0.000
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 score0.370
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.082
GPT teacher head0.371
Teacher spread0.289 · 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

Citations27
Published2000
Admission routes1
Has abstractyes

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