Cardiorespiratory responses during arm ergometry, functional electrical stimulation cycling, and two hybrid exercise conditions in spinal cord injured
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to compare peak functional aerobic power (VO2 peak) across four different types of exercise: arm crank ergometry (ACE), functional electrical stimulation (FES) cycling, and two hybrid exercise conditions: FES cycling combined with ACE and FES rowing using a newly developed rowing device (ROWSTIM). METHODS: Five participants (C7-T12), four male paraplegics with neurologically complete spinal cord injury (SCI), and one male with neurologically incomplete SCI, underwent a progressive maximal peak oxygen exercise test to ascertain peak physical work capacity during arm cranking, FES cycling, FES cycling combined with arm cranking and FES rowing. ariables were significantly lower for FES cycling versus ACE, FES cycling combined with ACE and FES rowing measures (P < 0.05). However there were no significant differences between ACE, FES cycling combined with ACE and FES rowing. CONCLUSIONS: Preliminary results suggest that the ROWSTIM is as effective an exercise device or training tool for persons with SCI as ACE or combined FES-cycling and ACE, and more effective than FES-cycling. A larger sample size and further technological developments of the ROWSTIM are needed to demonstrate the efficacy of rowing over other hybrid exercise modalities and ACE.
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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 | 0.000 |
| 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".