Advances in bioadhesives for meniscal repair: A comprehensive review and criteria for the ideal candidate
Bibliographic record
Abstract
The latest studies agree that meniscal tears that require surgery should be repaired whenever possible to avoid early-onset osteoarthritis secondary to meniscectomy. Unfortunately, there are several limitations associated with meniscal sutures, making it difficult to put into practice the theory behind the concept of saving the meniscus. Meanwhile, there is an exponential growth in the use of tissue adhesives for surgery, but finding one suited to meniscal repair remains a struggle. This review has two main goals (1) to compile the various bioadhesives used in this field and (2) to list the criteria for an ideal meniscal bioadhesive. The review was conducted in PubMed, Google Scholar, and Web of Science in November 2023 without date restrictions. The inclusion criteria were: Studies published in English and focusing on meniscal repair using bioadhesives. The exclusion criteria were: Studies published in languages other than English. Adhesives used in combination with sutures, as the aim was to determine the adhesive's capabilities for meniscal repair alone. Synthetic adhesives such as polycyanoacrylates, polyethylene glycol, polyurethanes, and polyesters. Among the 11 bioadhesives found, fibrin is the only one that has been studied in humans. There are advantages and disadvantages to all the bioadhesives identified but none that fully meet the requirements for meniscal repair. The anatomy of meniscal tissue is complex and poses unique challenges that are compounded by arthroscopic stresses. The future of meniscal repair probably lies in combining the advantages of several bioadhesives, and this area should be the focus of future research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".