Computational Modeling of Orthopedic Screw Pullout Performance According to ASTM F543: A Validation Approach Based on ASME V&V40 Standard and Food and Drug Administration Guidance
Bibliographic record
Abstract
Abstract Computational modeling and simulation (CM&S) is increasingly used to complement or replace traditional bench testing in the design, evaluation, and regulatory assessment of orthopedic devices. For orthopedic bone screws, pullout performance is commonly assessed through ASTM F543 testing, but this process can be costly and time-consuming. Finite element (FE) models, when subjected to rigorous verification, validation, and uncertainty quantification, can serve as surrogates to reduce reliance on experimental testing while supporting regulatory submissions. This study presents an end-to-end methodology for establishing the credibility of a device-agnostic FE model simulating screw pullout in accordance with ASTM F543. The model replicates the mechanical interaction between a bone screw and synthetic bone foam, with the quantity of interest being the maximum pullout force. Its context of use (CoU) is defined as a full surrogate for physical testing, and its associated use risk was determined to be medium-high, based on its role in device safety and performance assessment. Credibility activities were defined using the ASME V&V40 framework and the 2023 Food and Drug Administration (FDA) CM&S credibility guidance. Twenty-five credibility factors were reviewed, and high ratings were achieved for 19, reflecting a high degree of rigor across verification, calibration, validation, and applicability evidence. This work illustrates how a risk-informed credibility framework can be applied to establish a validated computational model of a standardized test in the medical device field, supporting its use as a surrogate for bench testing in both development and regulatory contexts.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".