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Record W2440591128 · doi:10.1111/jch.12840

High Blood Pressure 2016: Why Prevention and Control Are Urgent and Important. The World Hypertension League, International Society of Hypertension, World Stroke Organization, International Diabetes Foundation, International Council of Cardiovascular Prevention and Rehabilitation, International Society of Nephrology

2016· article· en· W2440591128 on OpenAlexaff
Norm R.C. Campbell, Tej K. Khalsa, Daniel T. Lackland, Mark L. Niebylski, Peter M Nilsson, Kimbree A. Redburn, Marcelo Orías, Xinhua Zhang, Louise M. Burrell, Masatsugu Horiuchi, Neil R Poulter, Dorairaj Prabhakaran, Agustín J. Ramiréz, Ernesto L. Schiffrin, Rhian M. Touyz, Ji‐Guang Wang, Michael A. Weber

Bibliographic record

VenueJournal of Clinical Hypertension · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsYork UniversityLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersBritish Heart FoundationInternational Society of Hypertension
KeywordsMedicineFoundation (evidence)LeagueStroke (engine)Blood pressureControl (management)Diabetes mellitusIntensive care medicineLawManagementInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Increased blood pressure (BP) is the second leading risk factor for death and disability globally according to the Global Burden of Disease Study.1 This work is an updated version of the World Hypertension League (WHL) 2014 Hypertension Fact sheet2 (www.whleague.org). Dr Mark Niebylski and Kimbree Redburn are paid WHL consultants but report no other conflicts. Dr Ji-Guang Wang reports receiving research grants and consulting fees from several BP-measuring device companies including A&D, AVITA, Honsun, Omron, and Rossmax but reports no other conflicts. Dr Michael Weber is a consultant for and received travel support from Omron but reports no other conflicts. All other primary authors including those from WHL and the International Society of Hypertension report no conflicts of interest.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.063
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.044
GPT teacher head0.285
Teacher spread0.240 · 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

Citations38
Published2016
Admission routes1
Has abstractyes

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