Assessment of Pre-Operative Functional Differences in Patients Undergoing Total and Partial Knee Arthroplasties
Bibliographic record
Abstract
Abstract Background: Osteoarthritis (OA) is a prevalent joint disease causing significant disability, particularly in the knee often treated end-stage with joint replacement surgery. While partial knee arthroplasty (PKA) is noted for quicker recovery and better functionality compared to total knee arthroplasty (TKA), its underutilization highlights a gap in surgical decision-making, driven by a lack of objective data on pre-operative functional differences. Methods: This prospective observational study, conducted from November 2023 to April 2024 at St. Joseph’s Healthcare Hamilton, included 34 end-stage OA patients scheduled for knee arthroplasty. Participants underwent pre-operative functional assessments using markerless motion capture technology to analyze gait and mobility during walking and sit-to-stand tests. Results: The study found no significant differences in basic gait and sit-to-stand metrics between the PKA and TKA groups at a preferred pace. However, at a faster pace, PKA patients demonstrated greater adaptability, showing significant increases in peak stance knee flexion, knee flexion excursions, and stride length, compared to TKA patients whose gait patterns remained consistent across speeds. Conclusion: PKA patients exhibit greater functional adaptability in their pre-operative state, suggesting potential underestimation of their capabilities in current surgical evaluations. Incorporating varied-pace walking tests in pre-operative assessments may provide deeper insights into functional capabilities, influencing more tailored surgical decisions and potentially increasing the application of PKA in suitable candidates.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".