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Record W2094171854 · doi:10.1155/2014/953094

The Impact of Mood and Anxiety Disorders on Incident Hypertension at One Year

2014· article· en· W2094171854 on OpenAlexafffund
Simon Bacon, Tavis S. Campbell, André Arsenault, Kim Lavoie

Bibliographic record

VenueInternational Journal of Hypertension · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsUniversité du Québec à MontréalUniversity of CalgaryConcordia UniversityHôpital du Sacré-Cœur de MontréalMontreal Heart Institute
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchHeart And Stroke Foundation Of QuebecHeart and Stroke Foundation of Canada
KeywordsMedicineAnxietyMoodMood disordersPsychiatryBlood pressureEssential hypertensionDepressed moodAnxiety disorderClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

Background. Studies assessing the association between psychological factors and hypertension have been equivocal, which may reflect limitations in the assessment of psychological factors. Purpose. To assess the relationship between mood and anxiety disorders, measured using a psychiatric interview, and 1-year incident hypertension. Methods. 197 nonhypertensive individuals undergoing exercise stress testing at baseline provided follow-up data at 1 year. Baseline assessments included a structure psychiatric interview (PRIME-MD), physician diagnosis of hypertension, and measured blood pressure. At follow-up, hypertension status was assessed via self-reported physician diagnosis. Results. Having an anxiety disorder was associated with a 4-fold increase in the risk of developing hypertension (adjusted OR = 4.14, 95% CIs = 1.18-14.56). In contrast, having a mood disorder was not associated with incident hypertension (adjusted OR = 1.21, 95% CIs = 0.24-5.86). Conclusions. There are potential mechanisms which could explain our differential mood and anxiety findings. The impact of screening and treatment of anxiety disorders on hypertension needs to be explored.

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)0.001
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 score0.534
Threshold uncertainty score0.205

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.022
GPT teacher head0.314
Teacher spread0.293 · 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

Citations73
Published2014
Admission routes2
Has abstractyes

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