Is Robotic TKA Having Added Advantage over Conventional TKA? A Comparative Study of Functional and Radiological Outcome of Robotic versus Conventional Total Knee Arthroplasty
Bibliographic record
Abstract
Abstract Objective Total knee arthroplasty (TKA) is most commonly performed procedure in patients who are not showing improvement in pain, activities of daily living, and quality of life by conservative modalities. Precise component implantation and soft tissue management is required to achieve desired outcome following TKA. 1.3% patients remain disappointed due to persistent pain, 24% due to instability, and 2.5% due to malalignment following TKA. Robotic TKA is associated with the use of customized implants and bone cuts leading to precise component implantation and reduced deviation from mechanical axis in coronal, transverse, and sagittal plane and proper soft tissue management. This study compares conventional against robotic TKA in terms of clinical, functional, and radiological outcome. Materials and Methods This is a prospective randomized control trial carried over period of 3 years where patients were selected on the basis of inclusion and exclusion criteria and were randomly divided into both groups and compared using their pre- and postoperative radiological and functional outcomes as well as intraoperative and postoperative complications and statistical significance of difference was calculated. Results There was no significant difference in terms of ROM, KOOS (Knee Injury and Osteoarthritis Outcome Score), (Knee Society Score) KSS, Eq. 5D, (Western Ontario and McMaster Universities Osteoarthritis Index) WOMAC, and (visual analog scale) VAS scores while we found significant difference in mechanical axis deviation, femoral and tibial implant alignment in both planes. Discussion Advantages of using robotic TKA are customized preoperative planning, implants, cuts, accuracy of the intraoperative procedure, and radiological superiority with no significant differences in clinical and functional outcomes. In fact, robotic TKA is associated with steep learning curve, increased cost, and operative time. Still there are no added complications caused by it.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".