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Record W3014546698 · doi:10.1097/hjh.0000000000002439

Attended versus unattended automated office blood pressure measurement in the diagnosis and treatment of hypertension

2020· article· en· W3014546698 on OpenAlexaff
Martin G. Myers, Alejandro de la Sierra, Michael Roerecke, Janusz Kaczorowski

Bibliographic record

VenueJournal of Hypertension · 2020
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversité de MontréalHealth Sciences CentreUniversity of TorontoCentre for Addiction and Mental HealthSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAmbulatoryAmbulatory blood pressureBlood pressureClinical PracticeWhite coat hypertensionCardiologyInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

: The main advantage of automated office (AO)BP in hypertension screening is that it eliminates white-coat effect seen in routine office practice, with readings similar to awake ambulatory (A)BP. Recent studies examining the possible equivalence between AOBP recorded with and without research staff present have reported attended systolic AOBP to be 5.8 mmHg higher. Moreover, attended automated SBP readings in 27 211 patients in clinical practice were 25 mmHg higher than awake ABP. These data are consistent with the presence of staff increasing AOBP. In research studies, all types of office BP measurement at target SBP less than 130 mmHg were generally lower than awake ABP, whereas, in clinical practice, attended automated office BP was slightly higher than awake ABP. However, AOBP may still be preferred, if target BP is to be similar to 24-h ABP. Further research is needed to determine the optimum technique for recording office BP at target.

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)0.006
metaresearch head score (Gemma)0.014
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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.161
GPT teacher head0.288
Teacher spread0.127 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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