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Record W4220871864 · doi:10.3390/jcm11051455

Rethinking Resistant Hypertension

2022· review· en· W4220871864 on OpenAlexaff
Gabrielle Bourque, Swapnil Hiremath

Bibliographic record

VenueJournal of Clinical Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineStroke (engine)Intensive care medicineMineralocorticoid receptorKidney diseaseDiseaseAdverse effectHeart failureCardiologyMyocardial infarctionInternal medicineAldosterone

Abstract

fetched live from OpenAlex

Resistant hypertension is common and known to be a risk factor for cardiovascular events, including stroke, myocardial infarction, heart failure, and cardiovascular mortality, as well as adverse renal events, including chronic kidney disease and end-stage kidney disease. This review will discuss the definition of resistant hypertension as well as the most recent evidence regarding its diagnosis, evaluation, and management. The issue of medication non-adherence and its association with apparent treatment-resistant hypertension will be addressed. Non-pharmacological interventions for the treatment of resistant hypertension will be reviewed. Particular emphasis will be placed on pharmacological interventions, highlighting the role of mineralocorticoid receptor antagonists and sodium-glucose cotransporter-2 inhibitors and device therapy, including renal denervation, baroreceptor activation or modulation, and central arteriovenous fistula creation.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.473
GPT teacher head0.496
Teacher spread0.023 · 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 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

Citations20
Published2022
Admission routes1
Has abstractyes

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