Assessing implant position and bone properties after cementless total knee arthroplasty using weight-bearing computed tomography
Bibliographic record
Abstract
BACKGROUND: Weight-bearing CT (WBCT) scanners are growing in availability and provide the capability of three-dimensional imaging while a joint is under load. This may be particularly useful in relation to personalized total knee arthroplasty (TKA) with cementless implants. The objective of the present study was to evaluate the utility and inter-observer repeatability of WBCT in assessing patients with cementless TKA in a loaded position. METHODS: Forty patients who underwent primary TKA approximately 3 years previously and received one of two cementless implant systems were recruited, including two subjects with bilateral TKA, for a total of 42 knees. All subjects underwent examination of their knee with WBCT while standing, thereby loading the indicated knee. Lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), hip-knee-ankle angle (HKAA), and joint line obliquity (JLO) were measured on full length radiographs and the WBCT exams by two observers. Femoral and tibial component rotation was measured on WBCT. Greyscale values representing bone density were assessed in five identically sized regions of interests in both the femur and the tibia on WBCT. RESULTS: Inter-observer agreement for alignment was good (95% ICC: 0.87). Inter-observer agreement for femoral component rotation was moderate (95% ICC: 0.67) and for tibial component rotation was good (95% ICC: 0.84). Inter-observer agreement for femoral greyscale values was good (95% ICC: 0.87) and for tibial greyscale values was excellent (95% ICC: 0.97). CONCLUSION: Cementless TKA can be assessed postoperatively using WBCT to measure implant position and bone density in a functional, loaded joint position with good inter-observer repeatability.
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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 | 0.001 | 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".