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Record W2318152542 · doi:10.1097/hco.0000000000000071

Multidisciplinary approaches to the management of high blood pressure

2014· review· en· W2318152542 on OpenAlexafffund
Sherilyn K. D. Houle, Trish Chatterley, Ross T. Tsuyuki

Bibliographic record

VenueCurrent Opinion in Cardiology · 2014
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of AlbertaUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsMedicineMultidisciplinary approachPharmacistPsychological interventionHealth careHealth professionalsDisease managementCollaborative CareNursingFamily medicineIntensive care medicineAlternative medicinePrimary careHealth management systemPharmacyPathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Studies on collaborative and multidisciplinary approaches to the management of hypertension published in the past 2 years are summarized. Expanding scopes of practice for nonphysician health professionals, a need to build capacity in the healthcare system, and a movement toward multidisciplinary care warrant an examination of the evidence in this area. RECENT FINDINGS: Multidisciplinary care for hypertension management, across the majority of studies identified, resulted in improved blood pressure (BP) outcomes and the timeliness of achieving treatment targets. Interventions involving therapeutic decision-making by nonphysician health professionals consistently resulted in significant BP improvements compared with usual care, whereas more passive approaches, such as education and lifestyle monitoring programs, were unable to significantly benefit participants' BP. SUMMARY: Our findings support recent efforts to integrate collaborative care approaches into chronic disease management, with the strongest evidence for pharmacist care. Expanding scopes of practice and clinical decision-making protocols for nurses, pharmacists, dietitians, and physiotherapists have the potential to further improve hypertension care.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.934
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.281
GPT teacher head0.390
Teacher spread0.109 · 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
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

Citations31
Published2014
Admission routes2
Has abstractyes

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