Total knee arthroplasty improves gait adaptability in osteoarthritis patients; a pilot study
Bibliographic record
Abstract
Background: Gait adaptability is of utmost importance for keeping balance during gait in patients with knee osteoarthritis, also after total knee arthroplasty (TKA). The aims of this explorative study are: (1) assess the effect of age, knee osteoarthritis and TKA on gait adaptability; (2) assess changes in gait adaptability pre-to post-TKA and (3) their relation to functional outcomes. Methods: Gait adaptability was measured using a Target Stepping Test (TST) in knee osteoarthritis patients before (preTKA) and 12 months after TKA (postTKA) and compared to asymptomatic old (AsOld) and young adults (AsYng). TST imposed an asymmetrical gait pattern with projected stepping targets at high walking speed. Gait adaptability was determined through stepping accuracy on the targets. The Oxford Knee Score (OKS) and Timed-Up-and-Go test (TUG) measured patients' physical function. Results: 12 preTKA, 8 postTKA, 18 AsYng, 21 AsOld were tested. Age showed no effect on TST-stepping accuracy. PreTKA showed worse TST-stepping accuracy compared to AsYng and AsOld (7.7; 6.2 cm difference). PostTKA showed an improvement of 52% in TST-stepping accuracy compared to preTKA (3.2 cm).Higher stepping accuracy preTKA predicted higher stepping accuracy post-TKA. In addition, low preTKA stepping accuracy predicted more improvement postTKA. Pre-to post-TKA improvement of stepping accuracy was related to improvement on the TUG (Beta = 0.17, p = 0.024), but not to OKS. Conclusions: Gait adaptability is improved following TKA in knee osteoarthritis patients and no longer significantly worse than asymptomatic adults. The relation of gait adaptability to function is shown by its relation to the TUG and shows to have predictive value pre-to post-TKA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".