Total knee replacement: intraoperative and postoperative kinematic assessment.
Bibliographic record
Abstract
BACKGROUND AND AIM: The main goals of the total knee arthroplasty (TKA) is to reduce the perceived pain and restore knee mobility and function in case of osteoarthritic knees joints. Literature shows how the three major causes of TKA failures are related to wear, loosening and instability and this is due to a problem of imbalance and malalignment. Intraoperative and postoperative kinematics analysis could be of benefit for improving surgery outcome. The aim of the present paper is to give an overview of the two set-up with the highest accuracy for intraoperative and postoperative TKA kinematics evaluation, currently in use at Istituto Ortopedico Rizzoli. Introperative and Postoperative Evaluation: For intraoperative evaluation it has been presented a navigation system with a specifically developed software, while for the postoperative it has been presented the roentgen stereophotogrammetric analysis (RSA). The navigation system consists in a laptop connected with an optoelectronic localizer (Polaris, Northern Digital Inc, Canada). Two reference arrays with passive optical markers and a marked probe are used to localize the knee joint in the 3D space and track the joint kinematics. The RSA is a radiographic technique used in orthopaedic field for measuring micromotion at bone/prosthesis interface or for joint kinematics evaluation. The RSA uses two X-ray sources synchronized with two digital flat-panels. CONCLUSIONS: The present paper shows that using the navigation system allows the surgeon to easily perform kinematic and alignment evaluation during TKA surgery while the RSA allows a quantitative evaluation of the joint kinematics during the recovery time.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".