Screening and Habituation of Functional Electrical Stimulation-Leg Cycle Ergometry for Individuals with Spinal Cord Injury: A Pilot Study
Bibliographic record
Abstract
OBJECTIVES: To document the screening process and develop the habituation procedures prior to commencing a functional electrical stimulation leg cycle ergometry (FES-LCE) exercise program for individuals with spinal cord injury (SCI). DESIGN: This was a prospective descriptive study of screening and habituation for FES-LCE exercise for people with chronic SCI (injury duration longer than one year). SETTING: : Tertiary rehabilitation center. PARTICIPANTS: Thirteen subjects with SCI (mean years since injury, 7; mean age, 34.8 years; injury range, C4-T10; 7 males). MAIN OUTCOME MEASURES: Time to attain target cycle speed for 30 continuous minutes. RESULTS: Six of the 13 subjects were not eligible to start the FES-LCE habituation program because of previous fragility fracture, excessive spasticity, pain, autonomic dysreflexia, or lack of electrical stimulation response because of lower motor neuron damage in individuals with low thoracic injuries (T11-12). Time to attain target cycle speed for 30 continuous minutes ranged from 30 to 779 minutes (1-31 training sessions). CONCLUSIONS: Almost half of the individuals interested in participating in the FES-LCE exercise program did not pass the screening phase. Although the habituation phase was time-consuming for most of the participants who were able to use the FES-LCE, all reported few adverse effects to using the device once they completed this phase of training.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".