New Algorithm for the Diagnosis of HypertensionCanadian Hypertension Education Program Recommendations (2005)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.011 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".