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Record W2541863282 · doi:10.1111/jch.12912

Hypertension Attitude PersPEctives and Needs (HAPPEN): A Real‐World Survey of Physicians and Patients With Hypertension in China

2016· article· en· W2541863282 on OpenAlexaff
Ross D. Feldman, Lisheng Liu, Zhaosu Wu, Yuqing Zhang, Xueqing Yu, Xinhua Zhang

Bibliographic record

VenueJournal of Clinical Hypertension · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineChinaBlood pressureFamily medicineHypertension treatmentPositive attitudeControl (management)Physical therapyInternal medicinePsychologySocial psychologyManagement

Abstract

fetched live from OpenAlex

The Hypertension Attitude PersPEctives and Needs (HAPPEN) survey was a real-world survey of cardiologists, nephrologists, and patients with treated hypertension at level 3 hospitals in China. It aimed to characterize the attitudes and behavior of physicians and patients and to identify possible causes of poor blood pressure (BP) control. Randomly selected participants (100 cardiologists, 30 nephrologists, 400 patients) completed face-to-face interviews investigating BP control rates, consulting behavior, prescribing patterns, and attitudes toward hypertension management. Perceived levels of BP control were high; 70% of physicians and 85% of patients believed that BP targets were achieved, despite only 31% of patients achieving targets. Physician satisfaction with control rates and patient satisfaction with treatment were high. Differences in perceived and actual levels of BP control may be driving therapeutic inertia. In combination with inadequate patient evaluation and support services, therapeutic inertia may contribute to poor BP control among patients with treated hypertension in China.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.329
Teacher spread0.271 · 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 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

Citations13
Published2016
Admission routes1
Has abstractyes

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