Investigation of a Novel <scp>PLGA</scp>/<scp>CaP</scp> Scaffold in the Healing of Tooth Extraction Sockets to Alveolar Bone Preservation in Humans
Bibliographic record
Abstract
BACKGROUND: It is expected that 40% to 60% of initial alveolar bone volume will be lost up to 6 months after tooth extraction. OsteoScaf(TM) (TRT, Toronto, ON, Canada) (poly (DL-lactide-co-glycololide/calcium phosphate [PLGA/CaP] scaffold) is a novel bone substitute material and represents a promising alternative for maintaining alveolar bone integrity in this clinical scenario. PURPOSE: Here it was hypothesized that OsteoScaf would reduce alveolar bone lost after tooth extraction in patient, acting as a clot-retention device. MATERIAL AND METHODS: A total of 10 patients (32 sockets) were included in the study, of which 16 sockets were grafted with OsteoScaf and 16 were used as control (coagulum alone). Cone beam computed tomography (CBCT) was performed both immediately following extraction and also at 120 days postoperatively, at which time biopsy samples were also harvested for histological analyses. RESULTS: Quantitative analysis of CBCT showed less bone resorption in the OsteoScaf groups, being 10.5% to 14.4% less bone lost in the center of the socket, 15.4% in the buccal region, and 12.6% in the palatal. Qualitative histological analysis showed new bone tissue in direct apposition to the scaffold - demonstrating its osteoconductive nature. CONCLUSION: OsteoScaf diminished the expected bone lost during the postextraction remodeling of the alveolar bone ridge at 120 days postextraction.
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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 | 0.001 | 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 | 0.001 |
| 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".