Differences of Post-Placement Bone Implant Contact (BIC) Value of Dental Implant Coated and Not Coated With Platelet Rich Plasma (PRP)
Bibliographic record
Abstract
OBJECTIVE: The success of a dental dental implant treatment focuses on a phenomenon called osseointegration. Evaluation of Bone Area (BA) and Bone-Dental implant Contact (BIC) through histomorphometric analysis is the most widely used parameter to measure osseointegration. The aim of this study was to see post-placement Bone Dental implant Contact (BIC) value of dental implant coated and not coated with PRP.MATERIALS & METHODS: This study was an experimental laboratory conducted at Laboratory of Veterinary Faculty, Hasanuddin University. The sample was baby buck rabbit, aged 4-8 months old, weight 1500–2000 gram, divided into 2 groups each group consist of 12 rabbit, control group not coated with PRP and treatment group coated with PRP. Data analysis using SPSS version 20.0 (SPSS Inc., Chicago, IL, USA).RESULTS: There was a significant difference between the mean BIC values and the 20% increase in BIC values in LP1, LP2 and LP3 between treatment and control group on day 0, 3, 7, and 14.CONCLUSIONS: There was a difference in the average of post-placement BIC value of dental implant coated and not coated with PRP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".