Inter‐observer reliability and anatomical landmarks for arm circumference to determine cuff size for blood pressure measurement
Bibliographic record
Abstract
Accurate arm circumference (AC) measurement is required for accurate blood pressure (BP) readings. Standards stipulate measuring arm circumference at the midpoint between the acromion process (AP) and the olecranon process. However, which part of the AP to use is not stipulated. Furthermore, BP is measured sitting but arm circumference is measured standing. We sought to understand how landmarking during AC measurement and body position affect cuff size selection. Two variations in measurement procedure were studied. First, AC was measured at the top of the acromion (TOA) and compared to the spine of the acromion (SOA). Second, standing versus seated measurements using each landmark were compared. AC was measured to the nearest 0.1 cm at the mid-point of the upper arm by two independent observers, blinded from each other's measurements. In 51 participants, the mean (±SD) mid-AC measurement using the anchoring landmarks TOA and SOA in the standing position were 32.4 cm (±6.18) and 32.1 cm (±6.07), respectively (mean difference of 0.3 cm). In the seated position, mean arm circumference was 32.2 (±6.10) using TOA and 31.1 (±6.03) using SOA (mean difference 1.1 cm). Kappa agreement for cuff selection in the standing position between TOA and SOA was 0.94 (p < 0.001). The landmark on the acromion process can change the cuff selection in a small percentage of cases. The overall impact of this landmark selection is small. However, standardizing landmark selection and body position for AC measurement could further reduce variability in cuff size selection during BP measurement and validation studies.
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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 | 0.051 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".