Post Doc Competition (Clinical/Best Practice Implementation) ID 1985647
Bibliographic record
Abstract
Background Functional electrical stimulation (FES) of paralyzed muscles can facilitate walking after spinal cord injury (SCI). Objective To test the orthotic effects of different FES walking protocols on lower joint kinematics in individuals with incomplete SCI. Methods Three adults with motor incomplete SCI participated in this study. Their lower extremity motor scores [LEMS (left, right)] were as follows: subject A: 25/25, subject B:15/25, subject C:9/23. They were asked to randomly complete four conditions of overground walking in a 4-meter walkway (6-10 times/condition) including no FES, and three bilateral FES walking protocols as follows: drop-foot (tibialis anterior stimulation), flexor withdrawal (common peroneal nerve stimulation), and multi-muscle stimulation (stimulation of quadriceps and gastrocnemius in the stance phase, and hamstring and tibialis anterior in the swing phase). The FES system obtained gait phase information from the two footswitches located under the individuals’ feet. Three-dimensional kinematic analysis was undertaken to measure minimum toe clearance (MTC), ankle, and hip range of motion (ROM) using a 100 Hz eight infrared camera (Vicon Motion Systems, Oxford, UK). Results The three FES-walking conditions significantly increased MTC compared to the no-FES condition in all participants. A significant decrease in ankle ROM was seen in the drop-foot (all subjects), multi-muscle (subjects A and C), and flexor withdrawal (subject A) stimulations. Hip ROM increased in the drop-foot (subjects B and C), flexor withdrawal (subject B), and multi-muscle (subject C) stimulations. Conclusions Three FES-walking protocols induced positive kinematic changes (increased hip flexion and toe clearance) during walking in subjects with incomplete SCI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.950 | 0.872 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".