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Record W2275014477 · doi:10.13034/jsst.v8i1.45

The Impact of Attire and Occupation on the Accuracy of Blood Pressure Measurements

2015· article· en· W2275014477 on OpenAlexvenueno aff
Arjun Pandey

Bibliographic record

VenueJournal of Student Science and Technology · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWhite coat hypertensionBlood pressureAmbulatory blood pressureWhite coatMedicineMasked HypertensionHealth careAmbulatoryInternal medicine

Abstract

fetched live from OpenAlex

White coat hypertension describes individuals with elevated blood pressure (BP) in medical facilities, such as clinics, and hospitals, but whose BP is normal when they are going about their daily activities. The purpose of this study was to assess whether certain types of healthcare providers are more accurately able to determine BP in comparison to the twenty four hour ambulatory blood pressure monitoring (ABPM), as well as to assess the effect of a healthcare provider’s attire on BP reading. The results show that BP readings were significantly higher when any of the healthcare providers wore a white lab coat. This suggests that attire of the healthcare provider has an impact on the BP readings. Cardiologists were most prone to causing white coat hypertension compared to nurses or cardiovascular technicians. It is therefore advised that alternate healthcare providers check BP to minimize the risk of erroneous BP readings and reduce the risk of white coat hypertension.

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)0.008
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.079
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.095
GPT teacher head0.383
Teacher spread0.288 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

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