COMPARISON OF BLOOD PRESSURE MEASUREMENT IN ROUTINE PRACTICE IN FRANCE AND CANADA
Bibliographic record
Abstract
Objective: Hypertension management in Canada is significantly better than in France, despite both countries having comparable standards of living. While differences in healthcare system organization and enhanced screening practices may partially explain this paradox, Canadian and European guidelines for hypertension management are quite similar. Variations in blood pressure measurement practices could also contribute to this discrepancy. To compare blood pressure measurement methods used in current practice in Canada and France and compare them with the respective recommendations. Design and method: A descriptive, cross-sectional survey conducted from June 2023 to March 2024 on a representative sample of 500 French general practitioners, using the same questionnaire employed in the Canadian survey of 2016. Results: For screening, Canadian doctors use an aneroid device in 54% vs. 73% of French doctors, and the automatic device in 43% vs. 25% of French doctors. The ABPM is used 3 times more often in Canada to confirm the diagnosis (15% vs. 5%). On the other hand, HBPM appears to be less widely used in Canada than in France (22% vs. 89%). During follow-up, Canadians and French use an aneroid device in 64% of cases, but 36% of Canadians take their measurements in a pharmacy vs. 0% in France.Conclusions: Canadian practices differ from those in France. In Canada, unlike France, pharmacists are involved in monitoring hypertensive patients. Tensiometers are still widely used in both countries, but mainly in France.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".