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Record W2793298617 · doi:10.1111/jch.13248

<scp>BASIS</scp>: The blood pressure awareness and insight scale

2018· review· en· W2793298617 on OpenAlexafffund
Philip Gerretsen, Julia Kim, Parita Shah, Lena C. Quilty, Thushanthi Balakumar, Fernando Caravaggio, Eric Plitman, Jun Ku Chung, Yusuke Iwata, Bruce G. Pollock, Satya Dash, Sanjeev Sockalingam, Ariel Graff‐Guerrero

Bibliographic record

VenueJournal of Clinical Hypertension · 2018
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsToronto General HospitalUniversity Health NetworkUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of Mental HealthNational Institutes of HealthOntario Mental Health FoundationCentre for Addiction and Mental Health
KeywordsMedicineScale (ratio)Blood pressureInternal medicine

Abstract

fetched live from OpenAlex

Impaired illness awareness or not accepting that one has hypertension (HTN) may be an important predictor of treatment adherence and optimal blood pressure control. The purpose of this study was to perform a systematic review of available instruments to evaluate HTN awareness, and subsequently present a novel scale that measures the core domains of subjective illness awareness in HTN. Based on the absence of any validated HTN specific measure identified through our review, the Blood Pressure Awareness and Insight Scale (BASIS) was developed (www.illnessawarenessscales.com). An online survey platform was used to collect data on 100 participants. BASIS showed good concurrent (r(98) = .65, P < 0.001) and discriminant validity, internal consistency (Cronbach's α = .75), and 1-month test-retest reliability (ICC = 0.77). BASIS is a comprehensive, easy-to-use instrument specifically designed to measure subjective HTN awareness. BASIS may be used in research studies and clinical practice to assess the impact of HTN awareness on treatment adherence and clinical outcomes.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.170
GPT teacher head0.408
Teacher spread0.238 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations11
Published2018
Admission routes2
Has abstractyes

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