Recombinant Human Transforming Growth Factor β-1 and Its Effects on Osseointegration
Bibliographic record
Abstract
Dental implant failures are still a common occurrence, especially in areas of poor bone quality. The purpose of this study was to observe whether the application of a growth factor, recombinant human transforming growth factor beta-1 (rhTGF-beta1), would improve the healing of bone adjacent to titanium dental implants. Four adult male Yucatan minipigs had four titanium dental implants placed into the inferior border of each hemimandible. On the control side, the implants were placed in standard fashion. On the experimental side, rhTGF-beta1 within a carrier gel was placed into the recipient site before implant placement. After a 6-week healing period, two implants from each side were evaluated by pull-out testing and histomorphometric analysis. The mean pull-out force for experimental implants was 1,124.01 N and 818.12 N (P = 0.5015) for controls. The mean percentage of bone between adjacent threads on the implant surface was 49.74% for the experimental group and 36.50% for controls (P = 0.0783). The mean percentage of bone-implant surface contact was 41.86% for the experimental implant sites and 24.60% for the control implant sites (P = 0.0452). The application of rhTGF-beta1 to implant sites appears to increase the amount of bone healing adjacent to a titanium dental implants in minipig mandibles at the 6-week period. Further studies are required to quantify better the amount of growth factor required and to study its effects over a broader period of time to see whether these differences are maintained.
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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 | 0.000 |
| 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".