Platelet-Rich Fibrin and Soft Tissue Wound Healing: A Systematic Review
Bibliographic record
Abstract
The growing multidisciplinary field of tissue engineering aims at predictably regenerating, enhancing, or replacing damaged or missing tissues for a variety of conditions caused by trauma, disease, and old age. One area of research that has gained tremendous awareness in recent years is that of platelet-rich fibrin (PRF), which has been utilized across a wide variety of medical fields for the regeneration of soft tissues. This systematic review gathered all the currently available in vitro, in vivo, and clinical literature utilizing PRF for soft tissue regeneration, augmentation, and/or wound healing. In total, 164 publications met the original search criteria, with a total of 48 publications meeting inclusion criteria (kappa score = 94%). These studies were divided into 7 in vitro, 11 in vivo, and 31 clinical studies. In summary, 6 out of 7 (85.7%) and 11 out of 11 (100%) of the in vitro and in vivo studies, respectively, demonstrated a statistically significant advantage for combining PRF to their regenerative therapies. Out of the remaining 31 clinical studies, a total of 8 reported the effects of PRF in a randomized clinical trial, with 5 additional studies (13 total) reporting appropriate controls. In those clinical studies, 9 out of the 13 studies (69.2%) demonstrated a statistically relevant positive outcome for the primary endpoints measured. In total, 18 studies (58% of clinical studies) reported positive wound-healing events associated with the use of PRF, despite using controls. Furthermore, 27 of the 31 clinical studies (87%) supported the use of PRF for soft tissue regeneration and wound healing for a variety of procedures in medicine and dentistry. In conclusion, the results from the present systematic review highlight the positive effects of PRF on wound healing after regenerative therapy for the management of various soft tissue defects found in medicine and dentistry.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".