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Chest pain is inversely associated with blood pressure during exercise among individuals being assessed for coronary heart disease

2007· article· en· W1997355659 on OpenAlexafffundabout
Blaine Ditto, Bianca D’Antono, Gilles Dupuis, Denis Burelle

Bibliographic record

VenuePsychophysiology · 2007
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalMcGill University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsBlood pressureChest painHeart ratePhysical therapyCardiologyMedicinePsychologyThreshold of painMcGill Pain QuestionnaireInternal medicine

Abstract

fetched live from OpenAlex

Acute and chronic increases in blood pressure have been related to decreases in pain perception. This phenomenon has been studied primarily using acute experimental pain stimuli. To extend the literature to naturalistic pain and in particular the problem of silent cardiac ischemia, this study examined the relationship between blood pressure and chest pain during exercise stress testing. Nine hundred seven (425 men, 482 women) individuals undergoing exercise stress testing for diagnosis of possible myocardial ischemia completed the McGill Pain Questionnaire (MPQ) immediately afterward and other questionnaires before and after testing. Blood pressure was measured before, during, and after exercise. Systolic blood pressure at the end of exercise was inversely related to a number of measures of pain such as total score on the MPQ. The relationship could not be explained by individual differences in exercise duration, medication use, sex, or other measured variable. In sum, the inverse relationship between blood pressure and sensitivity to pain that has been observed in other populations in experimental and naturalistic conditions was observed for chest pain during exercise. Blood pressure may contribute to episodes of silent ischemia.

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 score0.473
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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

Citations16
Published2007
Admission routes3
Has abstractyes

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