A one-year follow-up case series on gait analysis and patient-reported outcomes for persons with unilateral and bilateral transfemoral amputations undergoing direct skeletal fixation
Bibliographic record
Abstract
BACKGROUND: Direct skeletal fixation, a surgical technique enabling the attachment of an external prosthesis directly to the bone through a percutaneous implant, offers an enticing solution for patients with lower limb amputations facing socket-related issues. However, understanding of its impact on musculoskeletal function remains limited. METHODS: This study compares pre- and 1-year post-osseointegration surgery outcomes, focusing on patient-reported measures and musculoskeletal system function during level-ground walking. Two participants with unilateral transfemoral amputations and two participants with bilateral transfemoral amputations completed the questionnaire for transfemoral amputations (Q-TFA) and underwent gait analysis. Musculoskeletal modelling simulations were conducted. RESULTS: Results showed improved Q-TFA scores for all participants. Participants showed reduced amputated limb peak hip extension angles, flexion torques and contact forces at the push-off phase of the gait cycle. Post-operatively, hip adduction angles and abduction moments increased, indicating more natural gait patterns. Whilst one participant demonstrated increased post-operative walking speed, others walked more slowly. CONCLUSIONS: The study revealed diverse adaptation patterns after one year in individuals with transfemoral amputations transitioning to bone-anchored prostheses. Additional longer-term data is necessary to enable generalization and clinical implications of these results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".