Cardiovascular Response of Individuals With Spinal Cord Injury to Dynamic Functional Electrical Stimulation Under Orthostatic Stress
Bibliographic record
Abstract
In this pilot study, we examined how effectively functional electrical stimulation (FES) and passive stepping mitigated orthostatic hypotension in participants with chronic spinal cord injury (SCI). While being tilted head-up to 70 (°) from the supine position, the participants underwent four 10-min conditions in a random sequence: 1) no intervention, 2) passive stepping, 3) isometric FES of leg muscles, and 4) FES of leg muscles combined with passive stepping. We found that FES and passive stepping independently mitigated a decrease in stroke volume and helped to maintain the mean blood pressure. The effects of FES on stroke volume and mean blood pressure were greater than those of passive stepping. When combined, FES and passive stepping did not interfere with each other, but they also did not synergistically increase stroke volume or mean blood pressure. Thus, the present study suggests that FES delivered to lower limbs can be used in individuals with SCI to help them withstand orthostatic stress. Additional studies are needed to confirm whether this use of FES is applicable to a larger population of individuals with SCI.
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) | 0.001 | 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".