Inflationary noninvasive blood pressure monitoring reduces lower-limb pain during measurement
Bibliographic record
Abstract
Objective: This study aimed to investigate whether inflationary noninvasive blood-pressure measurement reduces pain during blood-pressure monitoring with the lower limbs compared to the conventional noninvasive measurement method.Methods: Healthy volunteers aged ≥ 18 years were recruited for the study. After seating the participants, a manchette was fitted onto each limb (upper limbs: YP-713T YAWARA CUFF2 13 cm; lower limbs: YP-715T YAWARA CUFF2 for thigh 19 cm, Nihon Kohden Tokyo, Japan). The inflationary and conventional noninvasive blood-pressure measurement devices (PVM-9901 and PVM-9901, Nihon Kohden, Tokyo, Japan) were connected, and the blood pressure was measured simultaneously at two points in the upper and lower limbs. After the measurement, the participants answered a questionnaire regarding the lower-limb pain, and the intensity of pain was evaluated using the visual analog scale.Results: The study included 111 healthy volunteers. The visual analog scale scores of the upper and lower limbs were significantly lower with the inflationary noninvasive blood pressure measurement device than with the conventional noninvasive blood pressure device (upper limbs: 25.6 ± 23.2 vs. 38.8 ± 27.5, p < .001 and lower limbs: 42.2 ± 25.1 vs. 54.2 ± 26.1, p < .01, respectively).Conclusions: We examined the effect of pain reduction on the lower limbs with inflationary noninvasive blood-pressure measurements in healthy volunteers. We conclude that inflationary noninvasive blood pressure measurement may reduce pain in the lower limbs during blood-pressure monitoring compared to a conventional noninvasive blood pressure measurement.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".