BIOMECHANICAL INDICATORS OF SUCCESS AFTER TOTAL KNEE ARTHROPLASTY PREDICTED WITH PREOPERATIVE CLINICAL MEASUREMENTS
Bibliographic record
Abstract
This study examined pre-operative measures to predict post-operative biomechanical outcomes in total knee arthroplasty (TKA) patients. Twenty-eight patients (female=12/male=16, age=63.6±6.9, BMI=29.9±7.4 kg/m2) with knee osteoarthritis scheduled to undergo TKA were included. All surgeries were performed by the same surgeon (GD) with a subvastus approach. Patients visited the gait lab within one-month prior to surgery and 12 months following surgery. At the gait lab, patients completed the knee injury and osteoarthritis outcome score (KOOS), a timed up and go (TUG), maximum knee flexion and extension strength evaluation, and a walking task. Variables of interest included the five KOOS sub-scores, TUG time, maximum knee flexion and extension strength normalized to body weight, walking speed, and peak knee biomechanics variables (flexion angle, abduction moment, power absorption). A Pearson's correlation was used to identify significantly correlated variables which were then inputted into a multiple regression. No assumption violations for the multiple regression existed for any variables. Pre-operative knee flexion and extension strength, TUG time, and age were used in the multiple regression. The multiple regression model statistically significantly predicted peak knee abduction moment, post-operative walking speed, and post-operative knee flexion strength. All four variables added statistically significantly to the prediction p<.05. Pre-operative KOOS values did not correlate with any biomechanical indicators of post-operative success. Age, pre-operative knee flexion and extension strength, and TUG times predicted peak knee abduction moment, which is associated with medial knee joint loading. These findings stress the importance of pre-surgery condition, as stronger individuals achieved better post-operative biomechanical outcomes. Additionally, younger patients had better outcomes, suggesting that surgeons should not delay surgery in younger patients. This delay in surgery may prevent patients from achieving optimal outcomes. Future studies should utilize a hierarchical multiple regression to identify which variables are most predictive.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".