The effects of functional electrically stimulated (FES)-arm ergometry on upper limb function and resting cardiovascular outcomes in individuals with tetraplegia: A pilot study
Bibliographic record
Abstract
Background: Functional electrically stimulated (FES)-arm ergometry has been shown to increase peak power output and aerobic capacity in individuals with cervical SCI. However, the functional benefits remain unknown. Objective: To determine the effects of FES-arm ergometry on exercise performance, upper limb function and resting cardiovascular function in individuals with tetraplegia. Methods: Five individuals (43.8 ± 15.4 years old) with SCI (C3-C5, AIS C-D, 14.0 ± 11.1 years post-injury) completed 12 weeks FES-arm ergometry. Exercise performance (time and distance to fatigue), perceived upper limb function [Capabilities of Upper Extremity Questionnaire (CUE), short form-Quadriplegia Index of Function Questionnaire (sf-QIF) and Spinal Cord Injury Spasticity Evaluation Tool (SCI-SET)] and resting mean arterial pressure (MAP) and heart rate (HR) were measured pre and post. Results: Following training, MAP significantly decreased (91.1 ± 14.0 to 87.7 ± 14.7 mmHg; p = 0.04), and there was a trend for an increased time to fatigue (804.6 ± 359.4 to 1483.8 ± 1110.2 sec; p = 0.08), distance to fatigue (3508.4 ± 3524.5 to 7412.6 ± 7773.1 m, p = 0.08) and the CUE scores pertaining to hand function (31.6 ± 12.8 to 38.0 ± 17.7; p = 0.07). Conclusion: Twelve-week FES-arm ergometry was associated with decreased resting MAP in individuals with tetraplegia, and may show promise as a means to increase exercise performance and hand function. Further research is required to verify these preliminary findings.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".