How do family physicians measure blood pressure in routine clinical practice
Bibliographic record
Abstract
Objective To describe the techniques currently used by family physicians in Canada to measure blood pressure (BP) for screening for, diagnosing, and treating hypertension. Design A Web-based cross-sectional survey distributed by e-mail. Setting Stratified random sample of family physicians in Canada. Participants Family physician members of the College of Family Physicians of Canada with valid e-mail addresses. Main outcome measures Physicians’ self-reported routine methods for recording BP in their practices to screen for, diagnose, and manage hypertension. Results A total of 774 valid responses were received, for a response rate of 16.2%. Respondents were similar to nonrespondents except for underrepresentation of male physicians. Of 769 respondents, 417 (54.2%) indicated that they used manual office BP measurement with a mercury or aneroid device and stethoscope as the routine method to screen patients for high BP, while 42.9% (330 of 769) reported using automated office BP (AOBP) measurement. The method most frequently used to make a diagnosis of hypertension was AOBP measurement (31.1%, 240 of 771), followed by home BP measurement (22.4%, 173 of 771) and manual office BP measurement (21.4%, 165 of 771). Ambulatory BP monitoring (ABPM) was used for diagnosis by 14.4% (111 of 771) of respondents. The most frequently reported method for ongoing management was home BP monitoring (68.7%, 528 of 769), followed by manual office BP measurement (63.6%, 489 of 769) and AOBP measurement (59.2%, 455 of 769). More than three-quarters (77.8%, 598 of 769) of respondents indicated that ABPM was readily available for their patients. Conclusion Canadian family physicians exhibit overall high use of electronic devices for BP measurement, However, more efforts are needed to encourage practitioners to follow current Canadian guidelines, which advocate the use of AOBP measurement for hypertension screening, ABPM and home BP measurement for making a diagnosis, and both AOBP and home BP monitoring for ongoing management.
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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 | 0.014 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".