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Record W2592501403 · doi:10.1016/j.cjca.2017.03.005

Hypertension Canada's 2017 Guidelines for Diagnosis, Risk Assessment, Prevention, and Treatment of Hypertension in Adults

2017· article· en· W2592501403 on OpenAlexaffvenueabout
Alexander A. C. Leung, Stella S. Daskalopoulou, Kaberi Dasgupta, Kerry McBrien, Sonia Butalia, Kelly B. Zarnke, Kara Nerenberg, Kevin C. Harris, Meranda Nakhla, Lyne Cloutier, Mark Gelfer, Maxime Lamarre-Cliché, Alain Milot, Peter Bolli, Guy Tremblay, Donna McLean, Sheldon W. Tobe, Marcel Ruzicka, Kevin D. Burns, Michel Vallée, G. V. Ramesh Prasad, Steven E. Gryn, Ross D. Feldman, Peter Selby, Andrew Pipe, Ernesto L. Schiffrin, Philip A. McFarlane, Paul Oh, Robert A. Hegele, Milan Khara, Thomas W. Wilson, S. Brian Penner, Ellen Burgess, Praveena Sivapalan, Robert J. Herman, Simon Bacon, Simon W. Rabkin, Richard E. Gilbert, Tavis S. Campbell, Steven A. Grover, George Honos, Patrice Lindsay, Michael D. Hill, Shelagh B. Coutts, Gord Gubitz, Norman R.C. Campbell, Gordon W. Moe, Jonathan G. Howlett, Jean-Martin Boulanger, Ally P.H. Prebtani, Gregory Kline, Lawrence A. Leiter, Charlotte Jones, Anne‐Marie Côté, Vincent Woo, Janusz Kaczorowski, Luc Trudeau, Ross T. Tsuyuki, Swapnil Hiremath, Denis Drouin, Kim Lavoie, Pavel Hamet, Jean‐Claude Grégoire, Richard Lewanczuk, George K. Dresser, Mukul Sharma, Debra J. Reid, Scott A. Lear, Grégory Moullec, Milan Gupta, Laura A. Magee, Alexander G. Logan, Janis M. Dionne, Anne Fournier, Geneviève Benoît, Luc Poirier, Raj Padwal, Doreen M. Rabi

Bibliographic record

VenueCanadian Journal of Cardiology · 2017
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsLibin Cardiovascular Institute of AlbertaCentre Hospitalier de l’Université de MontréalMontreal Heart InstituteHotchkiss Brain InstituteOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of British Columbia HospitalConcordia UniversityMcMaster UniversityCentre intégré de santé et de services sociaux de Chaudière-AppalachesPopulation Health Research InstituteHamilton Health SciencesHôpital du Sacré-Cœur de MontréalUniversity of ManitobaUniversity of SaskatchewanSt. Michael's HospitalSimon Fraser UniversityHôpital Charles-Le MoyneMontreal General HospitalJewish General HospitalMontreal Clinical Research InstituteCentre for Addiction and Mental HealthUniversité du Québec à MontréalUniversity of AlbertaUniversity of TorontoMemorial University of NewfoundlandUniversity Health NetworkUniversité LavalUniversité de MontréalUniversity of British ColumbiaCentre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-JeanMontreal Children's HospitalHôpital Maisonneuve-RosemontCentre hospitalier universitaire de QuébecCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalHeart and Stroke FoundationMcGill University Health CentreUniversité du Québec à Trois-RivièresUniversité de SherbrookeWestern UniversityCentre Hospitalier Universitaire Sainte-JustineMcGill UniversityVancouver Coastal HealthHôpital du Saint-SacrementDalhousie UniversityUniversity of OttawaUniversity of British Columbia, Okanagan CampusUniversity of Calgary
Fundersnot available
KeywordsMedicineDiureticBlood pressureRenovascular hypertensionCardiologyInternal medicineCalcium channel blockerLeft ventricular hypertrophyPillStroke (engine)Fibromuscular dysplasiaDiastoleSystolic hypertensionAmlodipineRenal arteryKidneyPharmacology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.010
metaresearch head score (Gemma)0.033
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.007
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0040.002
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0060.002

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 head0.102
GPT teacher head0.336
Teacher spread0.234 · 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

Citations322
Published2017
Admission routes3
Has abstractno

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