Effects of Photofunctionalisation on Osseointegration and Stability of Dental Implants: A Systematic Review
Bibliographic record
Abstract
Introduction: Dental implant osseointegration is crucial for the long term success of implant-supported restorations. Photofunctionalisation (PF), a novel surface modification technique, has been proposed as a means to enhance implant osseointegration. Aim: To evaluate the current evidence regarding the effects of PF on dental implant osseointegration. Materials and Methods: A comprehensive search was conducted in electronic databases, including PubMed, Directory of Open Access Journals, and Google Scholar, for studies published up until August 2022. The search strategy combined keywords related to dental implants, PF, and osseointegration. Two independent reviewers screened the titles, abstracts, and full texts of the identified studies, following predefined inclusion and exclusion criteria. Data extraction and quality assessment using the Cochrane Collaboration’s tool for randomised clinical trials, the ROBINS-I tool for non randomised studies, and the The Newcastle-Ottawa Scale (NOS) for observational studies were performed. Results: A total of five studies met the inclusion criteria and were included in the systematic review. The outcomes assessed included implant stability, osseointegration, and survival rates. The findings of the included studies suggested that PF of dental implants may promote osseointegration by enhancing early bone formation, increasing implant stability, and improving Bone-To-Implant (BIC) contact. Conclusion: The available evidence suggests that PF of dental implants may have a positive impact on osseointegration. However, due to the limited number of studies, further research is needed to provide more definitive conclusions regarding the clinical benefits of photofunctionalised dental implants in pathologically compromised bone sites.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".