Cardiovascular regulation: associations between exercise and head-up tilt
Bibliographic record
Abstract
It was hypothesized that faster cardiorespiratory kinetics during exercise are associated with higher orthostatic tolerance. Cardiorespiratory kinetics of 14 healthy male subjects (30 ± 4 years, 179 ± 8 cm, 79 ± 8 kg) were tested on a cycle ergometer during exercise with changing work rates of 30 and 80 W. Pulmonary oxygen uptake ([Formula: see text]) was measured breath-by-breath and heart rate (HR), mean arterial blood pressure (MAP), and total peripheral resistance (TPR) were measured beat-to-beat. Muscular oxygen uptake ([Formula: see text]) was estimated from HR and [Formula: see text]. Kinetic parameters were determined by time-series analysis, using cross-correlation functions (CCFmax(x)) between the parameter and the work rate. Cardiovascular regulations of MAP, HR, and TPR during orthostatic stress were measured beat-to-beat on a tilt seat. Changes between the minima and maxima during the 6° head-down tilt and the 90° head-up tilt positions were calculated for each parameter (Δtilt-up). [Formula: see text] correlated significantly with ΔTPRtilt-up (r = 0.790, p ≤ 0.001). CCFmax(HR) was significantly correlated with ΔHRtilt-up (r = –0.705, p = 0.002) and the amplitude in HR from 30 to 80 W (rSP = –0.574, p = 0.016). The observed correlations between cardiorespiratory regulation in response to exercise and orthostatic stress during rest might allow for a more differential analysis of the underlying mechanisms of orthostatic intolerance in, for example, patient groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".