Effect of PRF on extraction socket healing
Bibliographic record
Abstract
Extraction socket healing is a critical process in dental care that determines the success of subsequent dental treatments, such as implant placement. Platelet-rich fibrin (PRF) has emerged as a promising biomaterial for enhancing wound healing in various medical and dental applications. This abstract aims to provide a comprehensive overview of the effect of PRF on extraction socket healing, with a particular focus on the sample size of studies conducted in this area. PRF is an autologous blood-derived product rich in growth factors, cytokines, and platelets, which play key roles in tissue repair and regeneration. When applied to extraction sockets, PRF promotes accelerated wound healing by stimulating angiogenesis, enhancing cell proliferation, and modulating the inflammatory response. These biological effects contribute to improved soft tissue healing and osseous regeneration. A thorough review of the literature reveals that several studies have investigated the effect of PRF on extraction socket healing, with varying sample sizes. Sample sizes ranged from small-scale studies with fewer than 20 participants to larger-scale investigations involving over 100 subjects. The inclusion of sufficient sample size is crucial for obtaining statistically significant results and ensuring the generalizability of findings.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".