Validation and Precision of Model-Based Radiostereometric Analysis (MBRSA) for Total Ankle Arthroplasty
Bibliographic record
Abstract
BACKGROUND: The goal of this study was to design a RSA marker insertion protocol to evaluate the stability of the bone-implant interface of a TAA prosthesis, and to validate that this marker insertion protocol can be combined with MBRSA technology to provide clinically adequate precision in assessing the micromotion of the TAA prosthesis. METHODS: The Mobility™ Total Ankle System was used in this study. A marker placement protocol was developed with a Phantom Protocol. The Improved Marker Placement Protocol was used in 20 patients. Postoperative RSA double exams were taken. Condition Numbers (CN) were used to assess the marker distribution. The system precision was defined as the standard deviation of the double exams (MTE, MRE). MBRSA software was used to evaluate the double exams. RESULTS: The RSA marker insertion technique for the 20 {\it in vivo} cases provided satisfactory results. CNs in all subjects but one were below 50 mm(-1) and implied a desirable marker configuration. The tibial sphere MTE was 0.07 mm and the talar was 0.09 mm. The talar MRE was 0.51 degrees. CONCLUSION: The system precision for these {\it in vivo} TAA implants was within the normal range identified by RSA studies, and comparable to the existing TAA RSA studies. This study demonstrated a reliable RSA marker insertion technique in both the tibia and talus. The study confirms that the insertion and MBRSA technique allows the typical high precision demonstrated in other RSA studies (standard deviation less than or equal to 0.25 mm or 0.6 degrees). CLINICAL RELEVANCE: This method may allow more accurate assessment of prosthetic subsidence clinically.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".