Computer-assisted FluoroGuide navigation of unicompartmental knee arthroplasty.
Bibliographic record
Abstract
BACKGROUND: Most authorities recognize minimally invasive unicompartmental knee arthroplasty (UKA) as technically demanding with concerns regarding loss of implantation accuracy. We have previously reported on the potential inaccuracy of femoral intramedullary guides in UKA leading to poor component positioning. Our 3-dimensional analysis of alignment error showed that a short, narrow intramedullary rod inserted according to the manufacturer's specifications did not accurately find the direction of the anatomic axis, with errors occurring in both the coronal and sagittal planes. We sought to evaluate whether a fluoroscopic computer-assisted minimally invasive UKA procedure would improve the accuracy and precision in the placement of the femoral component in the coronal and sagittal planes compared with conventional surgery. METHODS: We performed a prospective study involving cohorts of 45 conventional versus 53 navigated UKAs. A single surgeon performed all surgeries over a 4-year period. RESULTS: Pain and knee function significantly improved in both surgical groups at 1 and 2 years after surgery. At a minimum of 1-year follow-up, radiographic evaluation revealed significant improvements in coronal alignment precision of the tibial component (p = 0.026) and sagittal alignment precision of the femoral component for the navigated group (p = 0.037). The use of a fluoroscopic computer-assisted technique did not significantly improve the accuracy of any of the alignment angles. CONCLUSION: We cannot justify the additional expense and complexity imposed by fluoroscopic navigation despite the observed improvements in alignment precision. Improved positioning precision may translate into a greater number of long-term functional results, but larger, longer-term studies are needed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".