Registry Assessment of Peripheral Interventional Devices (RAPID) ― Registry Assessment of Peripheral Interventional Devices Core Data Elements ―
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The current state of evaluating patients with peripheral artery disease and more specifically of evaluating medical devices used for peripheral vascular intervention (PVI) remains challenging because of the heterogeneity of the disease process, the multiple physician specialties that perform PVI, the multitude of devices available to treat peripheral artery disease, and the lack of consensus about the best treatment approaches. Because PVI core data elements are not standardized across clinical care, clinical trials, and registries, aggregation of data across different data sources and physician specialties is currently not feasible.Methods and Results:Under the auspices of the U.S. Food and Drug Administration's Medical Device Epidemiology Network initiative-and its PASSION (Predictable and Sustainable Implementation of the National Registries) program, in conjunction with other efforts to align clinical data standards-the Registry Assessment of Peripheral Interventional Devices (RAPID) workgroup was convened. RAPID is a collaborative, multidisciplinary effort to develop a consensus lexicon and to promote interoperability across clinical care, clinical trials, and national and international registries of PVI. The current manuscript presents the initial work from RAPID to standardize clinical data elements and definitions, to establish a framework within electronic health records and health information technology procedural reporting systems, and to implement an informatics-based approach to promote the conduct of pragmatic clinical trials and registry efforts in PVI. CONCLUSIONS: Ultimately, we hope this work will facilitate and improve device evaluation and surveillance for patients, clinicians, health outcomes researchers, industry, policymakers, and regulators.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it