Hip-Knee-Ankle (HKA) angle modification during gait in healthy subjects
Bibliographic record
Abstract
BACKGROUND: Achieving a neutral static Hip-Knee-Ankle angle (sHKA) measured on radiographs has been considered a factor of success for total knee arthroplasty (TKA). However, recent studies have shown that sHKA seems to have no effect on TKA survivorship. sHKA is not representative of the dynamic loading occurring during gait, unlike the dynamic HKA (dHKA). RESEARCH QUESTION: The primary objective was to see if the sHKA is predictive of the dynamic HKA (dHKA). A secondary objective was to document to what degree the dHKA changes during gait. METHODS: We analysed 3D knee kinematics during gait of a cohort of 90 healthy individuals with the KneeKG™ system. dHKA was calculated and compared with sHKA. Knees were considered "Stable" if the dHKA remained in valgus or varus for greater than 95% of the corresponding phase, and "Changer" otherwise. Patient characteristics of the Stable and Changer knees were compared to find associated factors. RESULTS: Absolute variation of dHKA during gait was 10.9 ± 5.3° for the whole cohort. The variation was less for the varus knees (10.3 ± 4.8°), than for the valgus knees (12.8 ± 6.1°, p = 0.008). We found low to moderate correlations (r = 0.266 to 0.553, p < 0.001) between sHKA and dHKA values for varus knees and no significant correlation for valgus knees. Twenty two percent (36/165) of the knees were considered Changers. The proportion of knees that were Changers was 15% of the varus versus 39% of the valgus (p < 0.001). SIGNIFICANCE: Lower limb radiographic measures of coronal alignment have limited value for predicting dynamic measures of alignment during gait.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".