Accuracy of home blood pressure readings: monitors and operators
Bibliographic record
Abstract
OBJECTIVE: To evaluate the accuracy of automated digital blood pressure monitoring devices and operators in the community. Also, we tested the effects of a simple education program, and looked for arm-arm differences. DESIGN: Subjects who had bought their own automated digital blood pressure monitor were recruited via an advertisement in the local newspaper. On arrival, they were asked to record their blood pressure exactly as they would at home. The investigator noted any technique deficiencies then corrected them. Blood pressures were then recorded by the investigator and the subject, on opposite arms, simultaneously, and repeated with the arms switched. Finally, subjects recorded their blood pressure again. The subjects' readings were compared to the average of monitor and mercury readings using Bland-Altman methods. RESULTS: A total of 80 subjects were tested. Before educating, subjects' systolic blood pressure (SBP) readings were +5.8+/-6.4 (standard deviation) mmHg greater than the mean of all readings, and diastolic blood pressure (DBP) were +1.3+/-4.0 mmHg; after educating they were +1.3+/-4.0 and -1.3+/-2.7 respectively. The monitors, as a group, were accurate, and met British Hypertension Society and AAMI highest standards. We found no differences among monitors that had been validated (n=26) and those that had not. There were differences between the arms: 5.3+/-5.2 mmHg for SBP and 3.4+/-3.3 mmHg for DBP. Most patients had never been informed by anyone of proper blood pressure measuring techniques. CONCLUSIONS: We conclude that home blood pressure measurement, as practiced in our community, is prone to error, mostly due to mistakes by the operator. These can easily be corrected, so that readings become more accurate. Attention should be paid to arm-arm differences.
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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 | 0.013 | 0.094 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".