Comparative Supply Chain Risk Management for Cardiovascular Devices (Implantable Cardioverter-Defibrillators): FDA, Health Canada, and EMA Approaches
Bibliographic record
Abstract
An Implantable Cardioverter-Defibrillator (ICD) is a high-risk, life-sustaining cardiovascular device that monitors heart rhythm and delivers electrical therapy to treat ventricular arrhythmias. Due to its implantable design and direct interaction with cardiac tissue, the ICD is classified as a high-risk device under most global regulatory frameworks (Bhanushali et al., 2025). This study examines the regulatory strategies of the U.S. Food and Drug Administration (FDA), Health Canada, and the European Union (EU) Medical Device Regulation (MDR). The objective is to identify how these authorities classify ICDs, evaluate pre-market evidence, and manage post-market oversight. The analysis highlights key similarities such as the adoption of risk-based classification systems and reliance on international standards like ISO 13485 (Quality Management Systems for Medical Devices) and ISO 10993 (Biological evaluation of medical devices) as well as significant differences in conformity assessment models and supplier audit structures (Bhanushali et al., 2025).
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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".