Inducible displacement of cementless total knee arthroplasty components with conventional and weight‐bearing CT‐based radiostereometric analysis
Bibliographic record
Abstract
Aseptic loosening remains one of the top causes of revision surgery of total knee arthroplasty (TKA). Radiostereometric analysis (RSA) is used in research to measure implant migration, however limitations prevent its clinical use. New methods have allowed the same measurements as RSA to be performed with computed tomography (CT) scanners (CT-RSA). The objective of this study is to determine inducible displacement measurements from weight-bearing computed tomography (WBCT) and conventional RSA to assess implant stability. Participants (n = 17) completed RSA exams in the supine and standing position, and WBCT exams in the seated (leg extended) and standing position. Double examinations were performed in the seated (WBCT) or supine (RSA) positions. Inducible displacements were measured with model-based RSA (MBRSA) for RSA exams, and a novel CT-RSA software, V3MA, for WBCT exams. Precision of each technique was calculated between double examinations. Precision data for tibial component total translations and rotations were 0.05 mm and 0.118°, respectively with WBCT-RSA, and were 0.108 mm and 0.269°, respectively with MBRSA. MTPM precision was 0.141 mm with WBCT-RSA and was 0.168 mm with MBRSA. Inducible displacement MTPM of the tibial component was 0.244 ± 0.220 mm with WBCT-RSA and 0.662 ± 0.257 mm with MBRSA. Inducible displacement measurements with MBRSA were significantly different from WBCT-RSA for tibial component anterior tilt (p = 0.0002). WBCT-RSA demonstrated comparable precision to MBRSA, and both techniques measured inducible displacements consistent with stable components. Clinical Significance: As the availability of WBCT increases, its use as an alternative to MBRSA is supported to measure the instantaneous fixation of implant 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 | 0.001 | 0.004 |
| 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 | 0.000 |
| 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".