Improving alignment in total knee arthroplasty: a cadaveric assessment of a surgical navigation tool with computed tomography imaging
Bibliographic record
Abstract
Purpose To investigate the accuracy of an imageless, optical surgical navigation tool to assist with femoral and tibial bone cuts performed during TKA.Patients and methods Six board-certified orthopedic surgeons participated in a laboratory cadaver investigation, performing femoral and tibial bone cuts with the assistance of a computer navigation tool. Femoral and tibial varus/valgus, tibial slope, femoral flexion, and both femoral and tibial rotation measurements from the device were compared with angular measurements calculated from computed tomography (CT) images of the knees.Results Measurements with the navigation tool were highly correlated with those obtained from CT scans in all three axes. For the distal femoral cut, the absolute mean difference in varus/valgus was 0.83° (SD 0.46°, r = 0.76), femoral flexion was 1.91° (SD 1.16°, r = 0.85), and femoral 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), posterior slope was 2.78° (SD 1.40°, r = 0.60), and rotation relative to the anteroposterior axis (posterior cruciate ligament to the medial third of the tibial tuberosity) was 2.98° (SD 2.54°, r = 0.79).Conclusion Utilization of an imageless navigation tool may aid surgeons in accurately performing and monitoring femoral and tibial bone cuts, and implant rotation in TKA and thus, more accurately align TKA components.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".