Diagnosing hypertension: Evidence supporting the 2015 recommendations of the Canadian Hypertension Education Program.
Bibliographic record
Abstract
OBJECTIVE: To highlight the 2015 Canadian Hypertension Education Program (CHEP) recommendations for the diagnosis and assessment of hypertension. QUALITY OF EVIDENCE: A systematic search was performed current to August 2014 by a Cochrane Collaboration librarian using the MEDLINE and PubMed databases. The search results were critically appraised by the CHEP subcommittee on blood pressure (BP) measurement and diagnosis, and evidence-based recommendations were presented to the CHEP Central Review Committee for independent review and grading. Finally, the findings and recommendations were presented to the Recommendations Task Force for discussion, debate, approval, and voting. The main recommendations are based on level II evidence. MAIN MESSAGE: Based on the most recent evidence, CHEP has made 4 recommendations in 2 broad categories for 2015 to improve BP measurement and the way hypertension is diagnosed. A strong recommendation is made to use electronic BP measurement in the office setting to replace auscultatory BP measurement. For patients with elevated office readings, CHEP is recommending early use of out-of-office BP measurement, preferably ambulatory BP measurement, in order to identify early in the process those patients with white-coat hypertension. CONCLUSION: Improvements in diagnostic accuracy are critical to optimizing hypertension management in Canada. The annual updates provided by CHEP ensure that practitioners have up-to-date evidence-based information to inform practice.
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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 | 0.047 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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".