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 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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".