Platelet‐rich fibrin to preserve alveolar bone sockets following tooth extraction: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Platelet-rich fibrin (PRF) can be used in the alveolar socket preservation (ARP). However, the hard tissue-regeneration property of PRF in alveolar socket preservation is still unclear. PURPOSE: To compare the new bone formation ratio between using PRF as a socket preservation material and normal wound healing, by means of histomorphometric analysis. MATERIALS AND METHODS: Thirty-three healthy volunteers were recruited and randomized into PRF and control group. Minimally traumatic extractions were performed. Eighteen patients were treated with ARP using PRF, while the rest were left to heal naturally. Bone specimens were harvested using trephine bur 2 months after the extraction process. Histomorphometric analysis of new bone formation area compared with total socket area was performed using the software Fiji Is Just Image J (version 2, GNU General Public License). RESULTS: Thirty-three volunteers were participated. Twenty-eight bone specimens were collected. The new bone formation ratio was higher in PRF group than in control group (31.33 ± 18% and 26.33 ± 19.63%, respectively). However, there was no statistically significant difference in the ratio between the PRF and control groups (P = .431). CONCLUSIONS: It may be concluded that the use of PRF in ARP does not statistically significant enhance new bone formation after tooth extraction compared to normal wound healing (P > .05).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".