Dynamics of Mucosal Integration of Machined versus Anodized Titanium Implants
Bibliographic record
Abstract
The long-term success of dental implants depends on the ability of soft tissues to form a protective barrier, limiting pathogen infiltration into peri-implant tissues. Here, we investigated the impact of an anodized surface modification on mucosal integration. Scanning electron microscopy and surface chemistry characterization were carried out on miniaturized implants. Following placement in fresh extraction sockets of mice, peri-implant tissues were examined at 4 time points. Histology along with quantitative immunohistochemistry for Keratin14, Vimentin, Laminin5, and CD68 were carried out on postimplant day (PID) 3 to assess early events in soft-tissue repair; on PID7, when peri-implant epithelialization was complete; at PID14, when osseointegration was complete; and at PID28, when soft-tissue maturation was nearing completion. In all cases, an intact junctional epithelium served as a reference. These analyses supported 3 conclusions: first, maturation of the peri-implant epithelium (PIE) is a protracted process, consistent with clinical observations. Second, maturation of the soft tissue-implant interface is slower than maturation of the bone-implant interface. Third, there is a benefit, albeit transient, to soft-tissue maturation around an anodized implant surface. Given its prolonged time course, strategies to improve and/or accelerate PIE maturation are likely to have significant clinical benefit.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".