Kinematic and Kinetic Analysis of a Stepping-in-Place Task in Below-Knee Amputee Children Compared to Able-Bodied Children
Bibliographic record
Abstract
It has been demonstrated that below-knee amputee (BKA) subjects use specific compensation strategies to overcome their physical limitations. Biomechanical studies emphasize that the motor strategies adopted by BKA adults differ between their amputated limb and their nonamputated limb and from those employed by able-bodied (AB) subjects. The purpose of this investigation was to compare the motor solutions used by control AB and BKA children during a stepping-in-place (SIP) task and to assess how they regulate the coordination of their nonamputated and amputated limbs during this task. Eight BKA children and eight AB children paired for gender, age, weight and height participated in our study. One-way analysis of variances (ANOVAs) were performed on peaks of angular excursion, moment, and power at the hip, knee, and ankle to compare motor strategies between the BKA and AB groups. The main results of our experiment showed that even if BKA and AB children did the task with almost the same kinematics, the kinetic data revealed completely different mechanisms of the two groups to achieve the SIP task, and BKA children had a symmetrical interlimb strategy. SIP, a simple task compared to gait at the level of neuro-musculoskeletal demands, could thus offer a transition task to physical therapists for below-knee recently-amputated children.
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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) | 0.000 | 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) | 0.001 | 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".