High coronal alignment accuracy and satisfactory early outcomes using augmented reality assisted kinematic alignment in total knee arthroplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Purpose Accurate component positioning in total knee arthroplasty (TKA) is critical for implant longevity and patient satisfaction. Augmented reality (AR)‐based navigation systems offer enhanced precision and intraoperative versatility. This study evaluated the accuracy of component positioning, implant sizing and short‐term clinical outcomes of a novel AR‐assisted navigation system (NextAR, Medacta International) in TKA using a modified kinematic alignment (KA) technique. Methods Forty‐one consecutive patients underwent primary TKA using AR‐assisted navigation with ≥12‐month follow‐up. Preoperative CT‐based 3D planning optimised cut orientation and component placement. All received a cemented medial pivot prosthesis (GMK Sphere) with full femoral resurfacing following a KA protocol. Tibial cuts were guided intraoperatively by real‐time ligament balancing. Planned versus achieved positions were compared on radiographs. Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), forgotten joint score (FJS) and range of motion (ROM) were recorded pre‐ and postoperatively and analysed using paired t ‐tests ( p < 0.05). Results The average difference between planned and postoperative alignment was 0.05° ± 0.76° for LDFA, 0.1° ± 0.6° for MPTA, –0.5 ± 1.7° for femoral component flexion, and 0.3° ± 1.3° for PTS. Root mean square errors were 0.75°, 1.23°, 1.73° and 1.34°, respectively. Postoperative HKA improved from 174.3° ± 3.4° to 177.8° ± 2.1° ( p < 0.001). Component size prediction was accurate in 100% of femurs and 95.1% of tibias. At final follow‐up (14.2 ± 2.3 months), WOMAC improved from 51.5 ± 16.7 to 13.6 ± 5.3, FJS from 26.2 ± 9.6 to 82.2 ± 7.4, flexion from 103.3° ± 17.4° to 129.4° ± 7.2° and extension from 3.3° ± 0.43° to 0.1° ± 0.28° (all p < 0.001). Conclusions AR‐based navigation in modified KA‐TKA ensured accurate LDFA restoration and femoral sizing, with good short‐term outcomes. Variability remained in MPTA, femoral flexion and PTS. Although no coronal recuts were needed, two tibial recuts for tight extension gaps highlight areas for system refinement. Level of Evidence Level IV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it