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.
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 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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".