Dental Implant Macro‐Design Features Can Impact the Dynamics of Osseointegration
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to compare the clinical performance of two dental implant types possessing a different macro-design in the in vivo pig model. MATERIALS AND METHODS: Titanium Aadva(TM) implants (GC, Tokyo, Japan) were compared with OsseoSpeed(TM) implants (Astra, Mölndal, Sweden), with the Aadva implant displaying significant larger inter-thread dimensions than the OsseoSpeed implant. Implants were installed in the parietal bone of 12 domestic pigs and left for healing for either 1 or 3 months. Implant osseointegration was evaluated by quantitative histology (bone volume relative to the tissue volume [BV/TV]; bone-to-implant contact [BIC]) for distinct implant regions (collar, body, total implant length) with specific implant thread features. The Wilcoxon-Mann-Whitney nonparametric test with α = 0.05 was performed. RESULTS: An inferior amount of bone enveloping the Aadva implant compared with the OsseoSpeed implant was observed, in particular at the implant body part with its considerable inter-thread gaps (p < .05). Concomitantly, the Aadva macro-design negatively affected the amount of bone in direct contact with the implant for this specific implant part (p < .05), and resulted in an overall impaired implant osseointegration at the initial healing stage (total implant length; 1-month healing; p < .05). CONCLUSION: Although the Aadva implant displayed a clinically acceptable level of osseointegration, the findings demonstrate that implant macro-design features can impact the dynamics of implant osseointegration. Consideration of specific implant macro-design features should be made relative to the biological and mechanical microenvironment.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".