Introducing a Novel Analysis Technique for Osseointegrated Dental Implants Retrieved 29 Years Postsurgery
Bibliographic record
Abstract
PURPOSE: To investigate osseointegration of oral implants, which were retrieved from a patient after 29 years in situ, we use novel three-dimensional analysis methods and visualization techniques that supplement conventional two-dimensional analysis. MATERIALS AND METHODS: The sample processing involved nondecalcification and embedment in resin. Conventional two-dimensional histomorphometrical methods were conducted. Additionally, the quantification was extended to three-dimensional by using synchrotron radiation micro-computed tomography (SRµCT) technique and two relevant visualization methods for the three-dimensional data were introduced. RESULTS: The three-dimensional results involved three-dimensional quantification and visualization of two implant samples with methods beyond state-of-the-art. Traditional two-dimensional histomorphometrical results revealed a mean bone-implant contact (BIC) of about 50%. In most samples, bone area (BA) was lower inside the treads compared with out-folded mirror images, which were confirmed by the three-dimensional quantification. The BIC along four selected regions showed highest percentages in the bottom/valley region and lowest in the thread-peak region. Qualitative observations revealed ongoing bone remodeling areas in all samples. The apical hole demonstrated high osseointegration. CONCLUSION: The novel techniques including an animation and an out-folding of BIC and BA enabled a simultaneous visualization of the three-dimensional material obtained from SRµCT data. However, the two-dimensional histological sections were needed for qualitative and quantitative evaluation of osseointegration and, thus, both methods are considered equally important.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".