A cadaveric investigation of the accuracy of a new, computer-assisted navigation system for total knee arthroplasty: A comparison with computed tomography imaging
Bibliographic record
Abstract
Despite the success of total knee arthroplasty (TKA), malalignment continues to be a problem which often leads to post-operative complications. The aim of this study was to investigate the accuracy of a novel, imageless, optical surgical navigation tool to assist with the alignment of femoral and tibial cuts performed during total knee arthroplasty. Six board-certified orthopedic surgeons performed TKA procedures on 9 cadavers (17 knees total), using a novel, imageless navigation system (Intellijoint KNEE, Intellijoint Surgical). Varus/valgus, femoral flexion, tibial slope, and rotation measurements from the device were compared with angular measurements calculated from post-operative computed tomography (CT) images. Navigation measurements were highly correlated with those obtained from CT scan in all three axes. For the femoral cuts, the absolute mean difference in varus/valgus was 0.83° (SD 0.46°, r = 0.76), in flexion was 1.91° (SD 1.16°, r = 0.85), and in rotation was 1.29° (SD 1.01°, r = 0.88) relative to Whiteside’s line and 0.97° (SD 0.56°, r = 0.81) relative to the posterior condylar axis. For the tibia, the absolute mean difference in varus/valgus was 1.08° (SD 0.64°, r = 0.85), anterior/posterior slope was 2.78° (SD 1.40°, r = 0.60), and rotation was 2.98° (SD 2.54°, r = 0.79). Intraoperative monitoring with the imageless navigation tool accurately measures femoral and tibial cuts in TKA and may help to increase component alignment.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".