Platelet‐Rich Plasma: A Study of the Variables that May Influence Its Effect on Bone Regeneration
Bibliographic record
Abstract
BACKGROUND: Currently, the use of platelet-rich plasma in bone regeneration is a real option, although more than one opinion has alerted us to the absence of clinical benefits. PURPOSE: Analysis of the factors able to modify the characteristics of the platelet preparation obtained by Curasan, Plasma Rich in Growth Factors (PRGF), Platelet Concentrate Collection System (PCCS) and SmartPrep systems, relating them to the type of clinical application and the final bone regeneration achieved. MATERIALS AND METHODS: A search was conducted in PubMed using the keywords "platelet-rich plasma," "PRP," "platelet rich growth factors," and "oral bone regeneration." Four widely accepted protocols for the obtention of PRP (above) were analyzed. Any clinical studies with controls, using the four preparation protocols and with a 4 to 6 weeks follow-up period were compared. The protocols were also grouped according to the type of PRP application: PRP-alone, with bone, or with bone substitutes. RESULTS: Bone regeneration was not achieved in any of the cases using PRP obtained by Curasan and PCCS systems, whereas PRP obtained by SmartPrep achieved it only in one in three published cases and PRGF in one in six. CONCLUSION: Based on the poor results observed in current literature, the use of PRP in oral surgery cannot be recommended.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".