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Record W2072131657 · doi:10.1097/hco.0b013e32833a3632

The Canadian effort to prevent and control hypertension: can other countries adopt Canadian strategies?

2010· review· en· W2072131657 on OpenAlexaffabout
Norm R.C. Campbell, Tobe Sheldon

Bibliographic record

VenueCurrent Opinion in Cardiology · 2010
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsSunnybrook Health Science CentreLibin Cardiovascular Institute of AlbertaUniversity of Calgary
Fundersnot available
KeywordsMedicineHypertension treatmentDisease controlControl (management)Position (finance)Environmental healthBlood pressureFinanceBusinessManagement

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To indicate the key elements of current Canadian programs to treat and control hypertension. RECENT FINDINGS: In the early 1990s Canada had a hypertension treatment and control rate of 13%. A Canadian strategy to prevent and control hypertension was developed and a coalition of national organizations and volunteers formed to develop increasingly extensive programs. The Canadian effort was largely based on annually updated hypertension management recommendations, an integrated and extensive hypertension knowledge translation program and an increasingly comprehensive outcomes assessment program. After the start of the annual process in 1999, there were very large increases in diagnosis and hypertension treatment coupled with dropping rates of cardiovascular disease. More recent initiatives include an extensive education program for the public and people with hypertension, a program to reduce dietary salt and a funded leadership position. The treatment and control rate increased to 66% when last assessed (2007-2009). SUMMARY: The study describes important aspects of the Canadian hypertension management programs to aid those wishing to develop similar programs. Many of the programs could be fully or partially implemented by other countries.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0010.001
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 head0.081
GPT teacher head0.358
Teacher spread0.277 · 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.

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

Citations54
Published2010
Admission routes2
Has abstractyes

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