Immediate implant placement with platelet rich fibrin as space filling material versus deproteinized bovine bone in maxillary premolars: A randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Several biomaterials have been suggested to augment the jumping gap during immediate implant placement. PURPOSE: The aim of this study was to compare the effect of xenograft or platelet derived growth factor (PRF) to graft the jumping gap in immediate implant placement in the maxillary premolar region. MATERIALS AND METHODS: Twenty patients underwent atraumatic extraction followed by immediate placement. The patients were equally divided into two groups. The first group received xenograft as a jumping gap filling material. The second group received PRF to graft the jumping gap. All patients received preoperative, immediate postoperative, and 6 months postoperative cone beam CT scan (CBCT). Implant stability quotient ISQ values were taken for the installed implants immediate postoperative and at 6 months. RESULTS: Implants receiving PRF as a jumping gap graft material demonstrated a significantly greater amount of crestal bone loss 1.85 ± 0.89 mm as compared to xenograft group 0.77 ± 0.32 mm (t = 3.52, p = 0.005). PRF group showed significantly greater reduction in buccopalatal direction 1.63 mm as compared to xenograft group 0.59 mm (t = 4014, p <0.001). ISQ values were similar immediately postoperative (t = 0.070, p = 0.945) while the ISQ values were significantly lower in PRF group as compared to xenograft graft at the six-month interval (t = 0.248, p = 0.023). CONCLUSION: The use of xenograft material as a jumping gap filling material resulted in superior results compared to PRF with regards to crestal bone loss, buccolingual socket reduction, and ISQ values.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".