Three‐Dimensional Biomechanical Gait Characteristics at Baseline Are Associated With Progression to Total Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: To determine if baseline 3-dimensional (3-D) biomechanical gait patterns differed between those patients with moderate knee osteoarthritis (OA) who progressed to total knee arthroplasty (TKA) and those that did not, and whether these differences had predictive value. METHODS: Fifty-four patients with knee OA had ground reaction forces and segment motions collected during gait. 3-D hip, knee, and ankle angles and moments were calculated over the gait cycle. Amplitude and temporal waveform characteristics were determined using principal component analysis. At followup 5-8 years later, 26 patients reported undergoing TKA. Unpaired t-tests were performed on baseline demographic and waveform characteristics between TKA and no-TKA groups. Receiver operating curve analysis, stepwise discriminate analysis, and logistic regression analysis determined the combination of features that best classified TKA and no-TKA groups and their predictive ability. RESULTS: Baseline demographic, symptomatic, and radiographic variables were similar, but 7 gait variables differed (P < 0.05) between groups. A multivariate model including overall knee adduction moment magnitude, knee flexion/extension moment difference, and stance-dorsiflexion moment had a 74% correct classification rate, with no overtraining based on cross-validation. A 1-unit increase in model score increased by 6-fold the odds of progression to TKA. CONCLUSION: In addition to the link between higher overall knee adduction magnitude and future TKA, an outcome of clear clinical importance, novel findings include altered sagittal plane moment patterns indicative of reduced ability to unload the joint during midstance. This combination of dynamic biomechanical factors had a 6-fold increased odds of future TKA; adding baseline demographic and clinical factors did not improve the model.
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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) | 0.001 | 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".