Load applied on bone-anchored transfemoral prosthesis: Characterization of a prosthesis� A pilot study
Bibliographic record
Abstract
The objectives of this study were to (1) record the inner-prosthesis loading during activities of daily living (ADLs), (2) present a set of variables comparing loading data, and (3) provide an example of characterization of two prostheses. The load was measured at 200 Hz using a multi-axial transducer mounted between the residuum and the knee of an individual with unilateral transfemoral amputation fitted with a bone-anchored prosthesis. The load was measured while using two different prosthetic knees, mechanical (PRO1) and micro processor-controlled (PRO2), during six ADLs. The characterization of the prostheses was achieved using a set of variables split into four categories, including temporal characteristics, maximum loading, loading slopes, and impulse. Approximately 360 gait cycles were analyzed for each prosthesis. PRO1 showed a cadence improved by 19% and 7%, a maximum force on the long axis reduced by 11% and 19%, and an impulse reduced by 32% and 15% during descent of incline and stairs compared with PRO2, respectively. This work confirmed that the proposed apparatus and characterization can reveal how changes of prosthetic components are translated into inner-prosthetic loading.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".