Neuromuscular adaptations after osseointegration of a bone-anchored prosthesis in a unilateral transfemoral amputee – a case study
Bibliographic record
Abstract
PURPOSE: Many individuals with a lower limb amputation experience problems with the fitting of the socket of their prosthesis, leading to dissatisfaction or device rejection. Osseointegration (OI)- the implantation of a shaft directly interfacing with the remaining bone- is an alternative for these patients. In this observational study, we investigated how bone anchoring influences neuromuscular parameters during balance control in a patient with a unilateral transfemoral amputation. MATERIAL AND METHODS: Center of pressure (CoP) and electromyography (EMG) signals from muscles controlling the hip and the ankle of the intact leg were recorded during quiet standing six months before and one and a half years after this patient underwent an OI surgery. Results were compared to a control group of nine able-bodied individuals. RESULTS: Muscle co-activation and EMG intensity decreased after bone anchoring, approaching the levels of able-bodied individuals. Muscle co-activation controlling the ankle decreased in the high-frequency range, and the EMG intensity spectrum decreased in the lower-frequency range for all muscles when vision was allowed. With eyes closed, the ankle extensor muscle showed an increased EMG intensity in the high-frequency range post-surgery. CoP length increased in the mediolateral direction of the amputated leg. CONCLUSIONS: These findings point to shifts in the patient's neuromuscular profile towards the one of able-bodied individuals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".