Bone tissue formation around two titanium implant surfaces placed in bone defects filled with bone substitute material or blood clot: A pilot study
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to evaluate the peri-implant bone tissue formation around titanium implants with different surface treatments, placed in bone defects filled or not with bone substitute material (BSM). MATERIALS AND METHODS: Ten animals were divided into two groups according to implant surface treatment. In each tibia, a bone defect was created followed by the placement of one implant. On the left tibia, the defect was filled with blood clot (BC), and on the right tibia, the defect was filled with biphasic hydroxyapatite/β-tricalcium-phosphate (HA/TCP) generating four subgroups: BC-N: blood clot and porous surface; BC-A: blood clot and porous-hydrophilic surface; HA/TCP-N: BSM and porous surface; HA/TCP-A: BSM and porous-hydrophilic surface. The animals were submitted to euthanasia 60 days after implant installation. After light-curing resin inclusion, the blocks containing the implant and the bone tissue were stained and evaluated by means of histomorphometry to assess the percentages of bone implant contact (% BIC). Data was normally distributed and the group differences were examined using the parametric tests of Two-Way ANOVA. RESULTS: The BC-A group presented the higher mean value of BIC (46.43%). The HA/TCP-A group presented the higher mean value of BIC. The porous-hydrophilic surfaces presented better results of BIC when compared to the porous surface in both conditions of defect filling. No statistically significant differences were found among all groups (95% confidence interval and P < .05). CONCLUSION: According to histomorphometric analysis, after 60-days in a rabbit model, hydrophilic and hydrophobic surfaces have the same behavior in the presence or absence of HA/TCP.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".