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Record W2808241675 · doi:10.1111/jch.13319

Antecedent rest may not be necessary for automated office blood pressure at lower treatment targets

2018· article· en· W2808241675 on OpenAlexaff
Tracey J. F. Colella, Anam Tahsinul, Hannah Gatto, Paul Oh, Martin G. Myers

Bibliographic record

VenueJournal of Clinical Hypertension · 2018
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsSunnybrook Health Science CentreToronto Rehabilitation InstituteUniversity of TorontoHealth Sciences CentreUniversity Health Network
Fundersnot available
KeywordsMedicineBlood pressureAmbulatory blood pressureAmbulatoryAntecedent (behavioral psychology)Rest (music)AnesthesiaBed restCardiologyInternal medicinePsychology

Abstract

fetched live from OpenAlex

In SPRINT (Systolic Blood Pressure Intervention Trial), use of the Omron 907XL blood pressure (BP) monitor set at 5 minutes of antecedent rest to record BP produced an automated office BP value 7/6 mm Hg lower than awake ambulatory BP at 27 months. The authors studied the impact on automated office BP of setting the Omron 907XL to 0 minutes instead of 5 minutes of rest in patients with readings in the lower normal BP range, similar to on-treatment BP in the SPRINT intensive therapy group. Patients (n = 100) in cardiac rehabilitation were randomized to three BP readings at 1-minute intervals using an Omron 907XL BP device set for 5 or 0 minutes of antecedent rest. Mean (±standard deviation) automated office BP (mm Hg) after 5 minutes of rest (120.2 ± 14.6/66.9 ± 8.6 mm Hg) was lower (P < .001/P < .01) than without rest (124.2 ± 16.4/67.9 ± 9.1 mm Hg). When target BP is in the lower normal range, automated office BP recorded without antecedent rest using an Omron 907XL device should be higher and closer to the awake ambulatory BP, compared with readings taken after 5 minutes of rest.

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)0.002
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.125
GPT teacher head0.397
Teacher spread0.272 · 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

Citations20
Published2018
Admission routes1
Has abstractyes

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