COMPUTATIONAL PREDICTION OF THE IMPACT OF SCAPULAR NOTCHING ON ARTICULAR WEAR RATE
Bibliographic record
Abstract
Wear of the intended articular surface and cup damage due to scapular impingement are known issues in reverse total shoulder arthroplasty (RTSA), but the relationship between them has not yet been fully investigated. This study includes the development of a computational wear model for the prediction of RTSA articular wear rates and implementation of this model to examine the interaction between cup damage due to scapular notching and articular wear. Three-dimensional models of 38mm diameter, 8.75mm cup depth, RTSA implants were constructed assuming 0.08mm radial clearance, and scapular notching damage was simulated as a circular arc with a width of 14mm and a depth of 3mm on the inferomedial edge of the cup. Finite element methods were used to ascertain the contact pressure across the articular surface of the humeral cup representing the relative motion and loading applied during previously performed in-vitro wear testing, which included a circumduction motion with an applied load ranging from 814–913N. Contact pressure was then input into a computational wear model which incorporated both the Archard wear model and the pressure-independent polyethylene (PE) wear model proposed by Liu et al. [1] to estimate the articular wear rate. The wear depth per million cycles (MC) across the surface of the cup was then integrated to determine the volumetric wear rate for both the unnotched cup and the cup with simulated scapular notching. The computational model predicted a slightly lower volumetric wear rate in the notched cup compared to the intact cup: a decrease of four percent using the Archard wear model (34.0mm3/MC unnotched vs. 32.6mm3/MC notched) and nine percent using the PE wear model (62.3mm3/MC unnotched vs. 56.6mm3/MC notched). This is consistent with the in-vitro results shown by Griffiths et al. who found a decrease of 26% (31.2mm3/MC unnotched vs. 23.0mm3/MC notched [2]), and Langohr et al. who found a decrease of eight percent (42.0mm3/MC unnotched vs. 38.8mm3/MC notched [3]). The similarity between the trends predicted by the computational model and the results of in-vitro wear testing demonstrates that the computational model is a valuable tool to investigate wear in RTSA implants. Its comparatively low cost and short time to yield results make it useful for evaluating changes in implant design and testing protocols before proceeding to full-scale in-vitro testing using only the most promising selections from the in-silico testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".