Dynamic stability metrics exhibit different periods of familiarization to treadmill walking
Bibliographic record
Abstract
The dynamic nature of gait heightens the risk of falling. Treadmill-based protocols are used to assess dynamic stability as they allow for uninterrupted gait. However, walking on treadmills differs from overground and individuals require time to adapt to the treadmill before reaching a steady-state gait. While familiarization was examined for gait kinematics, it remains uninvestigated for dynamic stability. As dynamic stability metrics quantify aspects of neuromuscular control, altered sensorimotor input from the treadmill would require familiarization to avoid confounding factors in the interpretation of fall risk. Dynamic stability metrics were assessed for twenty healthy young adults (18-30yrs) during two 10-min sessions of treadmill walking separated by one week. No familiarization in the mediolateral direction occurred but fluctuations in the anteroposterior direction for the Harmonic Ratio (session 1) and Margin of Stability (session 2) occurred. Fluctuations may reflect different strategies used to adjust to the treadmill. Specifically, participants altered step length and upper body posture in session 1 and 2, respectively. This may indicate that more than ten minutes are necessary for dynamic stability metrics to reach a steady-state. Further, treadmill exposure may modulate the motor strategies used to adjust dynamic stability during familiarization periods.
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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".