Reliability of Automated Blood Pressure Devices used by Hypertensive Patients
Bibliographic record
Abstract
Automated blood pressure (BP) devices are used by many hypertensive patients in Hong Kong, with or without medical advice. At two community clinics, we invited hypertensive patients aged between 40 and 70 years who used such a device to fill in a questionnaire and to have four sets of BP measurements, automated and mercury, at two visits. Of 290 hypertensive patients 120 fulfilled the criteria, and 73 of these agreed to participate. 53 devices measured arm BP, 21 measured forearm BP. The agreement between the mercury sphygmomanometer and the automated devices was poor, with average differences of 9.5 mmHg for systolic and 9.4 mmHg for diastolic and no clear advantage for either site of measurement. As a means of screening for BP >140/90 mmHg the sensitivity of the automated devices was 81% and the specificity was 80%. There were large variations in how often and under what circumstances the devices had been used. One-fifth of the devices had been acquired on medical advice but only 11% of the participants were aware of the three important conditions for operating such devices. Discussion of automated devices, their role and proper use, should now be part of routine hypertensive care.
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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 | 0.006 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".