Comparison between Digital Blood Pressure Monitors for Home Use (Wrist) and (Arm) with a Mercury Sphygmomanometer
Bibliographic record
Abstract
Blood pressure disease (BP) is one of the major public health problems around the world. There are many digital BP devices used at home. Doubts are usually raised about these devices. This study aimed to compare home (wrist) and (arm) digital blood pressure monitors with a mercury sphygmomanometer. This study was conducted at the University of Tabuk, comparing a digital BP meter (arm) and (wrist) with a properly calibrated mercury BP. A total of 100 randomly selected students aged 18 to 39 years were enrolled. Two blood pressure measurements for each person were recorded by all devices, and the values were recorded taking into account the environment of a person. The data were entered into MS Excel and analysis was done by using Social Sciences version 23.0. The normality of data was performed by the Shapiro-Wilk test and it was normally distributed. The mean and standard deviation were calculated for quantitative variables, while frequencies and percentages were provided for qualitative variables. Paired sample t-test was used to compare the mean values of systolic blood pressure (SBP) and diastolic blood pressure (DBP). Digital arm devices had sensitivity and specificity of 62.5% and 63%, respectively, compared to digital wrist devices, which had sensitivity and specificity of 75% and 56%, respectively. The readings of the digital blood pressure monitors were comparable to the readings of the mercury blood pressure monitor. Both devices could be considered indispensable tools for detecting hypertension at home and thus useful for early diagnosis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".