Dynamic input to determine hip joint moments, power and work on the prosthetic limb of transfemoral amputees
Bibliographic record
Abstract
BACKGROUND: Calculation of lower limb kinetics is limited by floor-mounted force-plates. OBJECTIVES: Comparison of hip joint moments, power and mechanical work on the prosthetic limb of a transfemoral amputee calculated by inverse dynamics using either the ground reactions (force-plates) or knee reactions (transducer). STUDY DESIGN: Comparative analysis. METHODS: Kinematics, ground reaction and knee reaction data were collected using a motion analysis system, two force-plates, and a multi-axial transducer mounted below the socket, respectively. RESULTS: The inverse dynamics using ground reaction underestimated the peaks of hip energy generation and absorption occurring at 63% and 76% of the gait cycle (GC) by 28% and 54%, respectively. This method also overestimated by 24% a phase of negative work at the hip (37%-56% GC), and underestimated the phases of positive (57%-72% GC) and negative (73%-98%GC) work at the hip by 11% and 58%, respectively. CONCLUSIONS: A transducer mounted within the prosthesis has the capacity to provide more realistic kinetics of the prosthetic limb because it enables assessment of multiple consecutive steps and a wide range of activities without the issue of foot placement on force-plates. CLINICAL RELEVANCE: The hip is the only joint an amputee controls directly to set the prosthesis in motion. Hip joint kinetics are associated with joint degeneration, low back pain, risk of falls, etc. Therefore, realistic assessment of hip kinetics over multiple gait cycles and a wide range of activities is essential.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".