An Inferential Model for Understanding the Effects of Demographic and Gait Factors and Their Interactions on the Human Gait Index: A Beta Regression Approach
Bibliographic record
Abstract
The gait index (GI), a valuable metric to assess human gait, incorporates clinically relevant parameters such as walking speed, knee angle, stride length, and stance-to-swing phase ratio. This index offers insights into an individual's gait pattern, aiding in the identification of subtle gait abnormalities and enabling continuous monitoring of gait changes over time. Building upon this foundation, the present study investigated the influence of specific gait parameters and demographic factors on the gait index, alongside their interaction effects. Analyzing data from 120 healthy individuals using beta regression models, we uncovered significant predictors and interaction effects shaping the Index. Our comparative assessment between Variable Dispersion Beta Regression (VDBR) and Fixed Dispersion Beta Regression (FDBR) models revealed VDBR's superiority over FPBR in capturing gait data heterogeneity. Our analysis revealed that while aging was correlated with decreased GI, gender and BMI exhibited limited individual impact. However, gait-specific predictors such as knee angle, stride length, walking speed, and stance-to-swing phase ratio significantly contributed to GI variability. Additionally, significant interaction effects were identified between knee angle and height normalized stride length, age and knee angle, and age and walking speed, highlighting the complex interplay between demographic and gait-related factors. These findings underscore the multifaceted nature of gait dynamics and offer valuable insights for clinicians, aiding in precise gait pattern assessment and informing the development of gait-related clinical practice, preventive care strategies, and rehabilitation programs. Overall, our research contributes to enhancing mobility and functionality in individuals with gait degradation by identifying significant predictors and interaction effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".