Validation of an Imageless Optical Computer-assisted Navigation System for Total Knee Arthroplasty
Bibliographic record
Abstract
Background: Restoration of the hip-knee-ankle (HKA) angle to within 3 of the neutral mechanical axis is considered a well-aligned total knee arthroplasty (TKA), with outliers associated with higher failure rates. Thus, efforts to improve intraoperative surgical accuracy are of strong clinical interest. This study evaluated the accuracy and safety of a novel, imageless, computer-assisted navigation system (CAS) for TKA. Methods: 112 consecutive patients who underwent primary TKA between January-December 2020 with 2 board-certified, high-volume orthopedic surgeons using the same imageless CAS were retrospectively reviewed. Patient age, BMI, sex, postoperative complications, and reoperations were collected. Two trained reviewers independently assessed tibial and femoral component mechanical alignment measurements in a standardized manner on postoperative full-leg AP and lateral radiographs. The primary outcome was mean absolute degrees of difference for each measurement compared to intraoperative CAS measurements. Outcomes were reported as means standard deviation. Results: 38%(N=43/112) of patients were male. Mean age was 698 years and mean BMI was 31.15.9. 71%(N=79/112) of patients had a well-aligned TKA (HKA within 3). The mean absolute difference was 1.51.2 for femoral coronal alignment, 1.00.8 for tibial coronal alignment, 2.21.5 for femoral flexion, and 1.81.6 for tibial slope. Two patients(1.8%) underwent reoperation; specifically, 1 patient received a 1-stage revision for periprosthetic joint infection 5 months postoperatively and the other underwent lysis of adhesions 9 months postoperatively for arthrofibrosis. Conclusions: This novel imageless CAS provides accurate readings within 2 for tibial and femoral coronal and sagittal alignment, and patients have low complication rates at early follow-up.
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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 | 0.014 |
| 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 | 0.001 | 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".