Effect of Platelet-rich Plasma on Implant Stability in the Mandible
Bibliographic record
Abstract
Background and aims. Plasma rich in growth factors (PRGFs) has been recently proposed as an aid to enhance regeneration of osseous and epithelial tissues in oral surgery. The purpose of this study was to determine the effect of local application of platelet-rich plasma (PRP) on implant stability measured by periotest. Materials and methods. A total of 24 implants were placed in the mandibles of 12 lower edentulous patients. In each patient, 2 implants were placed anterior to the mental foramen in bilateral canine sites. One implant in each patient was dipped in autogenous PRP before insertion (test group), while the other implant was not embedded in PRP (control group). Repeated stability measurements were carried out by periotest on the day of surgery and 1, 2, 4 and 8 weeks after surgery. Results. In both groups minimum periotest values (highest stability) were observed on the day of surgery and 8 weeks after surgery. The maximum periotest values (lowest stability) were observed in 4th week after surgery. Considering implant stability, no statistically significant differences were observed between the test and control groups at any time (P>0.05). In the PRP group, the difference in implant stability between the day of surgery and the 2nd and 4th weeks were statistically significant (P<0.05). Conclusion. Application of PRP on implant surface did not have any additional effect on implant stability in the mandible
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".