Improving Regions of Deviation Gait Symmetry Analysis With Pointwise t Tests
Bibliographic record
Abstract
The regions of deviation method has been proposed as a technique for identifying regions of the gait cycle where joint motion deviates from normal (Shorter et al., 2008). The original statistical analysis distinguished only peak values during stance and swing. In the current article, we extend the approach by examining deviations from normal throughout the entire gait cycle using pointwise t tests. These methods were demonstrated on hind-limb joint angles of 21 Labrador Retrievers without and with cranial cruciate ligament disease. Results were compared with peak difference analysis previously performed on these subjects. All points in the gait cycle where symmetry deviations were significantly affected by cranial cruciate ligament disease (via pointwise t tests) were defined as regions of deviation from symmetry. Discriminant function analysis was used to consider single subjects and validate that these regions were truly areas of difference between groups. Regions of deviation encompassed previously determined significant peak differences, while extending analysis to additional areas of asymmetry. Discriminant function analysis suggested that the region of deviation method is a viable approach for distinguishing motion pattern differences. This enhanced method may help researchers better understand the mechanisms behind lameness and compensation.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".