Avaliação de uma nova matriz tridimensional no processo de reparo de alvéolos dentários em humanos para preservação do rebordo alveolar
Bibliographic record
Abstract
Preserving alveolar bone architecture during socket healing is a significant clinical challenge. It is expected that 40% to 60% of the initial alveolar bone volume will be lost up to 6 months after tooth extraction. OsteoScaf is a novel bone substitute material developed at the University of Toronto and represents a promising alternative for maintaining alveolar bone integrity in this clinical scenario. It is a resorbable, osteoconductive, 3-phase composite scaffold comprising poly(lactide-coglycolide) and 2 osteoclast-resorbable calcium phosphate phases. Our hypothesis was that OsteoScaf lithomorphs (10x3mm cylinders), placed in fresh extraction sockets, would reduce post-extraction bone loss compared to sockets with coagulum alone (control). Thus, we placed OsteoScaf (of two different porosities -small and large pores) in fresh extraction sockets of maxillary anterior teeth in humans. 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. Cone Beam Computer Tomography (CBCT) was performed both immediately following extraction and also at 120 days postoperatively, at which time biopsy samples were also harvested for micro computer tomography (CT) and histological analyses. Quantitative analysis of the CT showed more bone trabeculae and smaller trabecular separation in the experimental group than control, specifically with small pore scaffolds. In addition, quantitative analysis of CBCT showed less bone resorption in the OsteoScaf TM groups, especially with the large pore scaffolds. Qualitative histological analysis showed new bone tissue in direct apposition to the scaffold -demonstrating its osteoconductive nature. Our observations showed that the OsteoScaf TM groups (small and large pores) reduced alveolar bone loss after 120 days of socket healing in humans. Grants: CAPES (Brazil) and TRT (Canada).
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".