Knee Biomechanics of Alternate Stair Ambulation Patterns
Bibliographic record
Abstract
PURPOSE: This study compared the kinematics and kinetics of the knee joint during traditional step-over-step (SOS) and compensatory step-by-step lead-leg (SBSL) and trail-leg (SBST) stair ambulation patterns. METHODS: Seventeen (M:9) healthy adults completed five trials of ascent and descent using three different stepping patterns: 1) SBSL, 2) SBST, and 3) SOS. Kinematics and kinetics were collected with an optoelectronic motion-tracking system and a force plate embedded into a four-step staircase. An inverse-dynamics link-segment model (QGAIT system) was used to calculate the net joint kinetics. RESULTS: During stair ascent, different peak anteroposterior (AP) forces were observed across all three stepping patterns (SOS > SBSL > SBST, P < 0.05). During ascent, the flexion moments of SOS (0.96 N x m x kg(-1)) and SBSL (0.97 N x m x kg(-1)) patterns were similar and much larger than the SBST moments (0.14 N x m x kg(-1)). In the descent conditions, the initial AP peak force for SOS was larger than that of SBSL and SBST. However, the second peak force for SOS (4.92 N x kg(-1)) and SBST (4.68 N x kg(-1)) were larger than SBSL (1.57 N x kg(-1)). During descent, the initial peak flexion moment for the SOS pattern was larger than SBSL and SBST, whereas during the second peak, SOS (1.05 N x m x kg(-1)) and SBST (1.11 N x m x kg(-)) were no different and larger than SBSL (0.18 N x m x kg(-1)). CONCLUSION: Overall, SBSL during ascent and SBST during descent had the highest loads. These results increase our understanding of alternative stepping patterns and have important clinical (reduction of loading on injured/diseased leg) and rehabilitation implications.
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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 | 0.000 | 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".