Impact of Graded Passive Cycling on Hemodynamics, Brain, and Heart Perfusion in Healthy Adults
Bibliographic record
Abstract
Purpose: Passive in-bed cycling (PC) can provide the benefits of early mobilization to critically ill patients who are unable to exercise actively. However, the effect of PC on global hemodynamics and perfusion of ischemia-prone organs, such as the brain and the heart, is unknown. Therefore, prior to studying the effects of PC in hemodynamically fragile critically ill patients, we characterized hemodynamic, brain blood flow and cardiac function responses to a graded increase in PC cadence in a cohort of healthy subjects. Methods: We measured global hemodynamic indices, middle cerebral artery velocity (MCAv), and cardiac function in response to a graded increase in PC cadence. Using 5 minute stages, we increased cadence from 5 to 55 RPM in increments of 10 RPM, preceded and followed by 5 minute baseline and recovery periods at 0 RPM. The mean values obtained during the last 2 minutes of each stage were compared within and between subjects for all metrics using repeated measures ANOVA. Results: 11 healthy subjects (6 females) completed the protocol. Between subjects, there was no change in MCAv, cardiac function or hemodynamics with the graded increase in cadence with one exception. There was a 7% increase in mean arterial pressure from baseline to 55RPM, that persisted through the recovery period. Across subjects, responses were heterogeneous, with some experiencing reduction in cardiac index, cerebral blood flow and cardiac function, especially at higher cadence. Conclusions: In healthy adults, increasing PC cadence increased mean arterial pressure in all subjects, while cardiac index, cerebral blood flow and cardiac function responses varied between subjects. Application of PC to critically ill patients must therefore consider individual variation in responses and tailor the PC to the patient. It is essential to further characterize these responses to PC in the critically ill prior to wide-scale clinical implementation.
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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 | 0.001 |
| 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) | 0.001 | 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".