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Record W4252079943 · doi:10.1177/014107680509800306

Reliability of Automated Blood Pressure Devices used by Hypertensive Patients

2005· article· en· W4252079943 on OpenAlexaff
William Chi Wai Wong, Ivan K L Shiu, Thomas M.T Hwong, James A. Dickinson

Bibliographic record

VenueJournal of the Royal Society of Medicine · 2005
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSphygmomanometerBlood pressureMedicineDiastoleReliability (semiconductor)Medical instrumentationInternal medicineCardiology

Abstract

fetched live from OpenAlex

Automated blood pressure (BP) devices are used by many hypertensive patients in Hong Kong, with or without medical advice. At two community clinics, we invited hypertensive patients aged between 40 and 70 years who used such a device to fill in a questionnaire and to have four sets of BP measurements, automated and mercury, at two visits. Of 290 hypertensive patients 120 fulfilled the criteria, and 73 of these agreed to participate. 53 devices measured arm BP, 21 measured forearm BP. The agreement between the mercury sphygmomanometer and the automated devices was poor, with average differences of 9.5 mmHg for systolic and 9.4 mmHg for diastolic and no clear advantage for either site of measurement. As a means of screening for BP >140/90 mmHg the sensitivity of the automated devices was 81% and the specificity was 80%. There were large variations in how often and under what circumstances the devices had been used. One-fifth of the devices had been acquired on medical advice but only 11% of the participants were aware of the three important conditions for operating such devices. Discussion of automated devices, their role and proper use, should now be part of routine hypertensive care.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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 designNot applicable
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

Citations10
Published2005
Admission routes1
Has abstractyes

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