Comparison of intra-arterial and manual auscultation of blood pressure during submaximal exercise in humans
Bibliographic record
Abstract
Blood pressure (BP) is a key measure of cardiovascular function, and accurate measurement is important to ensure proper clinical evaluation, diagnosis, and treatment. We compared intra-arterial (direct) and cuff auscultation (manual) measurement techniques at rest and during 2 levels of submaximal constant-load exercise (9 min at 40% and 75% maximum watts). Sixty-four adults (aged 29.0 ± 0.7 years; 48% male; height, 173.7 ± 1.2 cm; mass, 73.0 ± 1.7 kg; body mass index, 24.1 ± 0.4 kg·m(-2); body surface area, 1.87 ± 0.03 m(2)) participated in the study. At rest, low, and moderate intensity, direct measurement demonstrated higher systolic BP (SBP) and diastolic BP (DBP) (bias for SBP: 22, 31, and 27 mm Hg and for DBP: 5, 7, and 17 mm Hg; rest, low-, and moderate-intensity, respectively; p < 0.01). At rest, the correlation and agreement between the 2 methods was modest (SBP: r = 0.56, bias = +22.1 mm Hg; DBP: r = 0.53, bias = +4.9 mm Hg; p < 0.001). There was good correlation and agreement with SBP at low and moderate intensity; however, DBP demonstrated a weaker relationship (SBP: r = 0.74 and 0.74, bias = +30.2 and +26.8 mm Hg; DBP: r = 0.39 and 0.28, bias = +7.1 and +13.4 mm Hg; for low and moderate intensity, respectively; p < 0.001). Further, manual measurement demonstrated a greater slope from rest to moderate exercise for the relationship between pulse pressure (PP) and cardiac output (13.6 ± 0.4 vs 12.3 ± 0.4, p = 0.03). As exercise intensity increases, manual DBP tends to bias low compared with direct DBP, which, when combined with parallel increases in SBP, leads to no differences in PP between methods at moderate exercise. Because PP is used to calculate other cardiovascular parameters (mean arterial pressure, systemic vascular resistance), measurement technique and exercise intensity should be considered when using cardiovascular variables as outcome measures.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".