Intraoperative passive knee kinematics during total knee arthroplasty surgery
Bibliographic record
Abstract
Surgical navigation systems for total knee arthroplasty (TKA) surgery are capable of capturing passive three-dimensional (3D) angular joint movement patterns intraoperatively. Improved understanding of patient-specific knee kinematic changes between pre and post-implant states and their relationship with post-operative function may be important in optimizing TKA outcomes. However, a comprehensive characterization of the variability among patients has yet to be investigated. The objective of this study was to characterize the variability within frontal plane joint movement patterns intraoperatively during a passive knee flexion exercise. Three hundred and forty patients with severe knee osteoarthritis (OA) received a primary TKA using a navigation system. Passive kinematics were captured prior to (pre-implant), and after prosthesis insertion (post-implant). Principal component analysis (PCA) was used to capture characteristic patterns of knee angle kinematics among patients, to identify potential patient subgroups based on these patterns, and to examine the subgroup-specific changes in these patterns between pre- and post-implant states. The first four extracted patterns explained 99.9% of the diversity within the frontal plane angle patterns among the patients. Post-implant, the magnitude of the frontal plane angle shifted toward a neutral mechanical axis in all phenotypes, yet subtle pattern (shape of curvature) features of the pre-implant state persisted.
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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 | 0.000 |
| 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) | 0.000 | 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".