A New Algorithm for the Diagnosis of Hypertension in Canada
Bibliographic record
Abstract
Accurate blood pressure measurement is critical to properly identify and treat individuals with hypertension. In 2005, the Canadian Hypertension Education Program produced a revised algorithm to be used for the diagnosis of hypertension. Subsequent annual reviews of the literature have identified 2 major deficiencies in the current diagnostic process. First, auscultatory measurements performed in routine clinical settings have serious accuracy limitations that have not been overcome despite great efforts to educate health care professionals over several years. Thus, alternatives to auscultatory measurements should be used. Second, recent data indicate that patients with white coat hypertension must be identified earlier in the process and in a systematic manner rather than on an ad hoc or voluntary basis so they are not unnecessarily treated with antihypertensive medications. The economic and health consequences of white coat hypertension are reviewed. In this article evidence for a revised algorithm to diagnose hypertension is presented. Protocols for home blood pressure measurement and ambulatory blood pressure monitoring are reviewed. The role of automated office blood pressure measurement is updated. The revised algorithm strongly encourages the use of validated electronic digital oscillometric devices and recommends that out-of-office blood pressure measurements, ambulatory blood pressure monitoring (preferred), or home blood pressure measurement, should be performed to confirm the diagnosis of hypertension.
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 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".