Validation of imageless navigation in total knee arthroplasty using a postoperative radiographic approach
Bibliographic record
Abstract
INTRODUCTION: The integration of computer-assisted navigation systems (CASs) in total knee arthroplasty (TKA) procedures has gained popularity in recent years. However, additional validation of the accuracy of CAS feedback is necessary. We used short-length and full-length postoperative radiographs to quantify the differences between alignment parameters measured by a novel imageless CAS and alignment outcomes as evidenced on postoperative radiographs. MATERIALS AND METHODS: A retrospective analysis was conducted on prospectively collected data from a cohort of patients undergoing navigated primary TKA. Fifty-eight patients had met inclusion criteria, and intraoperative CAS measurements were obtained from device recordings. Alignment parameters were measured digitally and included femorotibial angle on short-length films and hip-knee-ankle axis, mechanical lateral distal femoral angle (mLDFA), and mechanical medial proximal tibial angle (mMPTA) on full-length films. These were compared between CAS and radiograph measurements using a 2-tailed t test. RESULTS: The mean mLDFA measured by the CAS was 0.7° ± 1.1°, compared with 1.3° ± 1.4° as measured on full-body radiographs (P = .1). The mean mMPTA measured by the CAS was 0.2° ± 1.0°, compared with 0.9° ± 1.4° as measured on full-body radiographs (P = .06). On average, radiograph and CAS measurements differed by 0.5° ± 1.5° for mLDFA and 0.7° ± 1.5° for mMPTA. The average postoperative hip-knee-ankle axis was 177.6° ± 2.1°, and the average femorotibial angle was 176.0° ± 9.6° as measured on radiographs. CONCLUSION: No significant differences in either average or individual measured values for mLDFA or mMPTA were observed between the intraoperative CAS measurements and alignment outcomes postoperatively. Our data highlight the clinical utility of CASs to accurately achieve intended TKA alignment objectives.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".