CAN KNEE MOTION PATTERNS BE USED AS A PREDICTIVE TOOL FOR IMPLANT STATUS AND LONGEVITY
Bibliographic record
Abstract
Introduction A thorough understanding of wear patterns and failure mechanisms of TKA components in the context of pre-revision knee kinematics is advantageous for component designers, manufacturers and surgeons alike. Traditional gait analysis provides an experimental technique to determine in vivo kinematics but is often limited by its cumbersome nature, infrastructure intensiveness and time. The recent introduction of the KneeKG (Emovi Inc, Canada) as a stand-alone knee motion tracking system which uses infrared technology provides a great opportunity to quickly, easily and routinely monitor patients at the clinical level, especially those being revised for component failure. This pilot study was conducted to examine pre-revision knee kinematics and subsequent wear patterns and failure mechanisms observed on the UHMWPE inserts upon retrieval in a cohort of TKA revision patients. We hypothesize that motion patterns can provide surgeons a unique insight into the status of the UHMWPE insert and implant longevity. Methods Patients requiring revision due to failure of the UHMWPE insert were recruited in this study after institutional ethical approval and written informed consent of the patients was obtained. Motion of the affected knee was quantified using a stand-alone infrared tracking system (KneeKG, Emovi Inc, Canada) whilst the patient was walking on a treadmill. All analyses were conducted within our institutional Physiotherapy Department. The KneeKG system is composed of passive motion sensors fixed on a validated knee harness, an infrared motion capture system (Polaris Spectra, Northern Digital Inc, USA) and a computer equipped with the Knee3D software suite (Emovi). Following application of the KneeKG trackers a calibration procedure was performed to identify joint centres and define a coordinate system on each body segment. After a treadmill habituation period of between 6 and 10 min, a trial was then conducted at the patient9s comfortable treadmill gait speed over 45 sec. Averaged clinical rotations and translations of the tibia as a function of gait cycle were output by the system, and a report highlighting and detailing biomechanical deficiencies as compared to a database of normal controls automatically generated. Following the scheduled revision surgery the retrieved components were formalin-fixed and brought to our laboratory for a routine retrieval workup. All revisions were performed by a single surgeon. Components were analysed using optical and scanning electron microscopy techniques for regions of polishing, burnishing, pitting, delamination, deformation, scratching and embedded debris. Wear maps and scores were generated and correlated with pre-revision kinematics for each patient. Results The KneeKG was successfully applied to patients in this pre-revision scenario, requiring less than 30 minutes to complete per case. Variations in knee kinematics have been observed, and the analysis of retrieved components is ongoing. Discussion This study has demonstrated that knowledge of pre-revision knee kinematic patterns can provide a unique insight into wear and failure mechanisms of the UHMWPE liner. Whilst this study is currently limited by a relatively small sample size, recruitment is continuing with a view to the possible generation of odds ratios for UHMWPE insert failure mechanisms based on kinematic signatures.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".