Alarming Prevalence of Emergency Hypertension Levels in the General Public Identified by a Hypertension Awareness Campaign
Bibliographic record
Abstract
BACKGROUND: Hypertension is a major cause of mortality and morbidity today. The "silent" nature of hypertension makes it critical to determine its prevalence and its severity in the general public and to identify strategies to identify people unaware of its presence. A mobile hypertension awareness campaign was created to: (i) determine the prevalence and types of hypertension in an urban North American center, (ii) increase hypertension awareness, and (iii) identify reasons for lack of therapy adherence. METHODS: Mobile clinics were provided at shopping malls, workplaces, hospitals, and community centres to measure blood pressure in the public. Blood pressure recordings were done on a voluntary basis. RESULTS: Of 1097 participants, 50% presented with high blood pressure which was higher than expected. Of particular clinical significance, an unexpectedly large number of participants (2%) exhibited a hypertensive urgency/emergency. Most of these people were not adherent to medications (if their hypertension was detected previously), were unaware of their hypertensive state, and/or unwilling to acknowledge or ignored the clinical significance of the extremely high blood pressure readings. Reasons for lack of adherence included: denial, being unaware of health consequences, and proper management of hypertension. CONCLUSIONS: A relatively large segment of an urban population lives unaware of severe emergency levels of hypertension. A public mobile hypertension clinic provides a valuable strategy for identifying hypertension in the general public and for knowledge translation of hypertension 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".