Validation of In Vivo Linear and Volumetric Wear Measurement for Reverse Total Shoulder Arthroplasty Using Model‐Based Radiostereometric Analysis
Bibliographic record
Abstract
ABSTRACT Presently, polyethylene wear measurement of reverse total shoulder arthroplasty (rTSA) is restricted to in vitro, in silico, and retrieval analysis, with no method for the quantification of in vivo wear of well‐functioning implants. The purpose of this study was to validate the use of model‐based radiostereometric analysis (MBRSA) as a measurement tool for in vivo rTSA wear using a phantom setup. Six additively manufactured polyethylene inserts were fabricated, one unworn control and five to represent known wear patterns, and individually fit within the rTSA components. Each insert was imaged using standard radiostereometric techniques and analyzed using MBRSA. From the position and orientation estimation provided by MBRSA, a micro‐computed tomography model of the control insert was virtually placed within the metaphyseal tray. The apparent intersection of the glenosphere into the insert was recorded as wear. This method enables wear measurements with a linear precision of 0.21 mm and a bias of 0.36 ± 0.13 mm, and a volumetric precision of 49.3 mm3, with a bias of 48.9 ± 24.3 mm3. This technique allows for the in vivo measurement of polyethylene wear without the requirement of marker beads or baseline radiographs, expanding the potential for in vivo wear measurements to larger populations and retrospective analysis. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:1620–1627, 2019.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".