Sex Differences in BP From an Automated Oscillometric Compared With Manual Device
Bibliographic record
Abstract
BACKGROUND: Automated blood pressure (BP) devices may be less accurate in females than males, but this requires further investigation. This study aimed to determine sex differences in automated BP, measured with a single brand and model of device, compared with manual BP, with a focus on cuff sizes and associations with measures of adiposity. METHODS: Automated (Omron HEM-907XL) and manual BP were taken sequentially in a random order among a subsample of participants attending the US National Health and Nutrition Examination Survey, 2017 to 2018. Anthropometry and dual-energy x-ray absorptiometry were used to record body size and composition. Analyses, including multivariable regression to determine sex differences in BP, by cuff size, followed complex survey statistical principles. RESULTS: A total of 3735 participants (49.0% female [95% CI, 46.4-51.6], 45 years [43-46]) were included. In females, automated systolic BP (SBP) incrementally underestimated manual SBP across larger cuffs up to extra-large (-6.4 mm Hg [-8.0 to -4.9]). In males, automated SBP underestimated manual SBP only with extra-large cuffs (-2.4 mm Hg [95% CI-3.9 to -0.9]). Underestimation by automated SBP with extra-large cuffs was independently associated with all measures of body size indicative of increased adiposity in both females and males. Hypertension classification from automated and manual SBP had moderate agreement for adult/large cuffs (weighted kappa range 0.66-0.79) but weak agreement for extra-large cuffs (0.55-0.58) for females and males. CONCLUSIONS: The automated device used in this study underestimated manual SBP at larger cuff sizes, which was associated with indices of adiposity. Poorer accuracy of automated BP in larger cuff sizes could contribute to inequitable BP-related health care for females and males and requires further investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".