Oral Surgical Site Infections and Wound Healing Associated with Silk Fibroin Sutures versus Alternative Suture Materials: A Systematic Review
Bibliographic record
Abstract
Introduction: Delayed or improper wound healing can lead to Surgical Site Infections (SSIs), which are associated with increased mortality, morbidity, readmission rates and healthcare costs. Dental sutures are routinely used to close wounds, promote haemostasis and prevent infection. Although non absorbable sutures are preferred for promoting wound healing and preventing infection, Silk Fibroin (SF) sutures are still used due to their affordability and favourable properties. However, their multifilament structure makes them susceptible to higher bacterial adherence. Aim: To compare the effectiveness of SF sutures in reducing SSIs and promoting wound healing with other suture materials used in dental procedures. Materials and Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 checklist. PubMed, University of Toronto libraries and the Web of Science (WoS) were searched using specific keywords until January 4, 2025. Data were extracted and a risk of bias assessment was performed using the Risk of Bias 2 (RoB 2) and Risk of Bias In Non randomised Studies - of Interventions (ROBINS-I) tools. Nine studies were included. Results: The study demonstrated that non resorbable multifilament SF sutures show high microbial adherence and prolonged wound closure time compared to other materials, due to their multifilament and braided structure. However, significant infections were rarely reported. Results regarding bleeding, pain and swelling varied across studies and were mostly non significant on day 7. Conclusion: Antiseptic or antibiotic coatings on SF sutures can reduce bacterial adherence and lower the risk of infection, especially given their significantly higher adherence compared to other sutures.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".