Overview of approval procedures for bioadhesives in the United States of America and Canada
Bibliographic record
Abstract
Bioadhesives are useful medical devices to help reduce postoperative complications and as adjuncts to sutures and staples in sealing wounds. Biomedical companies have been promoting research and development into new bioadhesives. As for other medical devices, translating promising candidates to market involves the need to pass through several regulatory steps, wherein their safety and effectiveness are evaluated and the proper reimbursements from payors are assessed. The regulatory procedures involve classification based on the risk factors, support studies, submission of applications to relevant authorities, procurement of certification, and finally commercialization, while keeping a track record of the post-market data. The importance of real-world data has been recently realized. The aim of this review is to focus on the translational goals, expectations, and necessities of medical devices focusing on the bioadhesives to be commercialized. It should aid researchers inspired to discover and market new bioadhesives in understanding the need for basic regulatory procedures behind their commercialization for medical usage, most importantly for internal medicine specifically in the United States of America, Canada, and Europe, in part. The key differences in the regulatory aspects among those are highlighted. Regulations keep changing with the introduction of new products and governmental laws. They are updated in this manuscript till March 2021.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".