Comparison of 5 Normalization Methods for Knee Joint Moments in the Single-Leg Squat
Bibliographic record
Abstract
Ratio scaling is the most common magnitude normalization approach for net joint moment (NJM) data. Generally, researchers compute a ratio between NJM and (some combination of) physical body characteristics (eg, mass, height, limb length, etc). However, 3 assumptions must be verified when normalizing NJM data this way. First, the regression line between NJM and the characteristic(s) used passes through the origin. Second, normalizing NJM eliminates its correlation with the characteristic(s). Third, the statistical interpretations following normalization are consistent with adjusted linear models. The study purpose was to assess these assumptions using data collected from 16 males and 16 females who performed a single-leg squat. Standard inverse dynamics analyses were conducted, and ratios were computed between the mediolateral and anteroposterior components of the knee NJM and participant mass, height, leg length, mass × height, and mass × leg length. Normalizing NJM-mediolateral by mass × height and mass × leg length satisfied all 3 assumptions. Normalizing NJM-anteroposterior by height and leg length satisfied all 3 assumptions. Therefore, if normalization of the knee NJM is deemed necessary to address a given research question, it can neither be assumed that using (any combination of) participant mass, height, or leg length as the denominator is appropriate nor consistent across joint axes.
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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 | 0.000 | 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) | 0.000 | 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".