Use of Real-World Data for Enhancing Model Credibility: Applications to Medical Device Development
Bibliographic record
Abstract
Abstract Evaluating the credibility of computational models used in medical device development is increasingly important as medical devices become more complex and modeling takes on a more critical role in the device development process. While bench-testing based comparisons are common for assessing model credibility and have many advantages, such as control over test specimens and the ability to quantify outputs, the credibility assessments performed with bench tests often do not evaluate the clinical relevance of key aspects of model form (such as boundary conditions, constitutive models/properties, and geometries) selected when simulating in vivo conditions. Real-world data (outcomes data generated through clinical use of a device) offer an opportunity to assess the applicability and clinical relevance of a computational model. Although real-world data are frequently less controlled and more qualitative than benchtop data, real-world data are often a direct assessment of a particular clinical complication and therefore of high clinical relevance. Further, real-world data have the potential to reveal failure modes not previously identified in preclinical failure modes analysis, thereby motivating testing advancements. To review the use of clinical data in medical device modeling, this paper presents a series of examples related to tibial tray fracture that incorporate varying levels of benchtop data and real world data when evaluating model credibility. The merits and drawbacks of the credibility assessment for each example are discussed in order to provide practical and actionable guidance on the use of real-world data for establishing and demonstrating model credibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".