Soft and Hard Tissue Response to Zirconia versus Titanium One‐Piece Implants Placed in Alveolar and Palatal Sites: A Randomized Control Trial
Bibliographic record
Abstract
BACKGROUND: Titanium (Ti) implants have been used in the last four decades to replace missing teeth. Alternatives to Ti such as zirconia (Zr) may offer aesthetic advantages and be more acceptable to patients and clinicians concerned about Ti allergy but must show equivalent biological acceptability to Ti. PURPOSE: The research aimed to investigate soft and hard tissue response to Ti and Zr implants in edentulous patients. MATERIALS AND METHODS: The research included 24 participants (Ti = 12, Zr = 12) restored with one-piece ball-abutment implants to support overdentures. Participants received four maxillary implants (two in the premolar alveolus, one off center in the alveolar midline, and one wide-diameter implant in the anterior median palate) and three mandibular implants (one in the midline and bilateral posterior implants). RESULTS: Success rates for both Ti and Zr implants were low, 67.9% for all alveolar implants and a survival rate of 50.0% for the palatal implants. Only 11 (52.4%) of 21 palatal implants survived the follow-up period. Peri-implant health was equivalent for Ti and Zr implants and showed no statistically significant changes from loading to the 1-year follow-up. Statistically significant differences were noted in radiographic bone level between Ti and Zr implants (p = .02), with Zr showing greater bone loss. CONCLUSIONS: Although the failure rates with the one-piece Zr implants were higher than with the Ti ones, suggesting that the former's clinical usage as in this study cannot be recommended, it should be borne in mind that the fault may also lie with the novel prosthodontic design which was used.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".