A surgeon’s primer on Unique Device Identifier (UDI) capture: Where do we stand on implant surveillance and how can we improve?
Bibliographic record
Abstract
The Unique Device Identifier (UDI) system was developed to improve the identification and tracking of medical devices throughout their lifecycle. Despite its potential to enhance patient outcomes, streamline supply chain management, and facilitate swift responses to device recalls, widespread adoption remains limited. In the absence of regulatory policies enforcing standardized UDI practices, this paper underscores the critical role surgeons play in implementing and advocating for UDI’s utility within their institutions. By reviewing published roadmaps and playbooks, we highlight key steps for successful adoption. We examine case studies from early adopter institutions—Mercy Health, Duke University Health System, and Kaiser Permanente—that have successfully integrated UDI systems, demonstrating tangible benefits. Recognizing the barriers to nationwide UDI implementation as well as the unique role of surgeons at the intersection of patient care and device innovation, we conclude by proposing practical, actionable steps for surgeons to drive change within their institution.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".