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Record W2969767313 · doi:10.1111/jch.13683

A step in the global effort to control hypertension: Fixed dose combination antihypertensive drugs

2019· article· en· W2969767313 on OpenAlexaff
Norm R.C. Campbell

Bibliographic record

VenueJournal of Clinical Hypertension · 2019
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineDirectiveBlood pressureIntensive care medicineFixed-dose combinationPharmacotherapyPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Several fixed dose combinations (FDCs) of antihypertensive drugs have recently been added to the World Health Organization model list of essential medications. FDCs have advantages in the management of hypertension compared to single drug tablets including improved adherence, greater blood pressure lowering and are associated with reduced cardiovascular complications. FDCs can also reduce ethnic, and age-related variation in blood pressure lowering and have similar or reduced adverse effects relative to single-drug therapy. Best hypertension control practices from the World Health Organization HEARTS program advocates the use of FDC in simple directive treatment protocols. FDC in simple directive protocols was viewed as a key success factor in the control of chronic infections (eg, tuberculosis, HIV). Unfortunately, implementing simple directive protocols with FDC has had substantial opposition from hypertension experts. Hypertension organizations and experts need to familiarize themselves with best practices in hypertension control, their supporting evidence and to become advocates.

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)0.013
metaresearch head score (Gemma)0.012
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: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.007
Open science0.0020.003
Research integrity0.0060.013
Insufficient payload (model declined to judge)0.0100.004

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 head0.053
GPT teacher head0.355
Teacher spread0.302 · 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
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

Citations8
Published2019
Admission routes1
Has abstractyes

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