Clinical Validation and Excellent Interobserver Agreement of Volumetric Matching Micromotion Analysis (V3MA) in Total Knee Arthroplasty
Bibliographic record
Abstract
CT-based radiostereometric analysis (CT-RSA) is an alternative to RSA to measure implant migration. We performed a clinical validation study using VoluMetric Matching Micromotion Analysis (V3MA) software for CT-RSA. The aims of this study were to assess the agreement between V3MA and Model-based RSA software (for RSA), and to determine the interobserver agreement in V3MA. On a subset of patients included in a clinical trial, knee prosthesis tibial implant migration was measured between 1 and 5 years postoperative with V3MA and Model-based RSA software. V3MA and Model-based RSA results were compared by assessing the mean differences and limits of agreement (mean ± 1.96* standard deviation) using Bland-Altman analysis. V3MA migration results of two observers were compared using intraclass correlation (ICC) and Bland-Altman analysis. Twenty-four patients were included in the analysis. The mean difference (limits of agreement [LOA]) was -0.14 mm [-0.88 to 0.60] for maximum total point motion (MTPM). LOA for translations and rotations did not exceed ±0.5 mm and ±1°, respectively. The ICC (95% confidence interval) for MTPM between observers was 0.995 (0.989-0.998), and the mean difference [LOA] was 0.04 mm [-0.17 to 0.24]. We showed that between 1 and 5 years postoperative, V3MA migration results were comparable to those of Model-based RSA for cemented tibial component migration in a clinical study. The interobserver variability showed excellent agreement for V3MA. Overall, V3MA is a valid alternative to Model-based RSA for the analysis of tibial component migration in TKA with medium-term follow-up.
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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 | 0.030 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".