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New Algorithm for the Diagnosis of HypertensionCanadian Hypertension Education Program Recommendations (2005)

2005· article· en· W2037490883 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAmerican Journal of Hypertension · 2005
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsUniversity of AlbertaUniversity of CalgaryHôtel-Dieu Grace HealthcareMemorial University of NewfoundlandSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAlgorithmComputer science

Abstract

fetched live from OpenAlex

Most national and international guidelines for diagnosing hypertension include 24-h ambulatory blood pressure monitoring (ABPM) and self (home) BP monitoring (SBPM) as optional methods for identifying hypertensive patients. However, none of the current guidelines have yet included ABPM or SBPM as fundamental tools for diagnosing hypertension, preferring instead to rely on conventional office readings recorded by mercury sphygmomanometry. During the past 10 years, clinical outcome studies have consistently reported 24-h ABPM and SBPM to be significantly better predictors of cardiovascular events compared with the office BP, even when recorded under "research conditions." Based on the available evidence, the Canadian Hypertension Education Program has now developed an algorithm for diagnosing hypertension that offers three options: 1) conventional office BP, 2) SBPM, or 3) 24-h ABPM. Out-of-office BP measurements are recommended, whenever feasible, to minimize both measurement error associated with mercury sphygmomanometry and the white coat effect experienced by some patients.

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.

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: Empirical · Consensus signal: none
Teacher disagreement score0.759
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.042
GPT teacher head0.306
Teacher spread0.264 · 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