Comparative Osseointegration Outcomes of Titanium vs. Zirconia Implants: A Schematic Assessment with Meta-Analysis of Bone-Implant Contact and Clinical Longevity
Bibliographic record
Abstract
Background: Previously, titanium implants were the most widely chosen option because they bond very well with the jawbone and have good strength. On the other hand, zirconia implants are now being used, giving patients improved appearance, better tissue compatibility, and fewer chances of bacterial infection. The review discusses how titanium and zirconia implants perform when placed in human subjects compared to each other in terms of osseointegration, survival rate, bone loss, and clinical outcomes. Methods: A PRISMA-aligned systematic review and meta-analysis were set up following specified protocols, but were not registered on PROSPERO. Until April 2025, study databases such as PubMed, Scopus, Web of Science, and Cochrane Library were searched for human studies. Some of the key outcomes assessed were BIC and bone level changes. The study evaluated the risk of bias using the Newcastle-Ottawa Scale and the Cochrane RoB tool. Using random effects, a meta-analysis reported the pooled odds ratios (OR) along with their confidence intervals (CI). Results: The review included 12 studies, which were randomized trials, cohorts, or case reports. When all the studies were pooled, it was found that there was no difference between titanium and zirconia implants in terms of BIC and clinical longevity (OR 1.09, 95% CI 0.84–1.41). Results revealed that zirconia implants were similar to titanium in terms of survival and surrounding bone support. Discussion: Overall, zirconia implants can give good outcomes and are a smart alternative to titanium, especially when aesthetics or sensitivity matter. Nonetheless, additional well-designed trials lasting for an extended time are needed to fully assess zirconia implants’ benefits and efficacy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".