Imageless optical navigation system is clinically valid for 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
Achieving optimal implant position and orientation during total knee arthroplasty (TKA) is a pivotal factor in long-term survival. Computer-assisted navigation (CAN) has been recognized as a trusted technology that improves the accuracy and consistency of femoral and tibial bone cuts. Imageless CAN offers advantages over image-based CAN by reducing cost, radiation exposure, and time. The purpose of this study was to evaluate the accuracy of an imageless optical navigation system for TKA in a clinical setting. Forty-two consecutive patients who underwent primary TKA with CAN were retrospectively reviewed. Femoral and tibial component coronal alignment was assessed via post-operative radiographs by two independent reviewers and compared against coronal alignment angles from the CAN. The primary outcome was the mean absolute difference of femoral and tibial varus/valgus angles between radiograph and intra-operative device measurements. Bland-Altman plots were used to assess agreement between the methods and statistically analyze potential systematic bias. The mean absolute differences between navigation-guided cut measurements and post-operative radiographs were 1.16 ± 1.03° and 1.76 ± 1.38° for femoral and tibial alignment respectively. About 88% of coronal measurements were within ±3°, while 99% were within ±5°. Bland-Altman analysis demonstrated a bias between CAN and radiographic measurements with CAN values averaging 0.52° (95% CI: 0.11°–0.93°) less than their paired radiographic measurements. This study demonstrated the ability of an optical imageless navigation system to measure, on average, femoral and tibial coronal cuts to within 2.0° of post-operative radiographic measurements in a clinical setting.
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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 | 0.001 |
| 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 | 0.000 |
| 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 it