Immediate Loading of Trabecular Metal‐Enhanced Titanium Dental Implants: Interim Results from an International Proof‐of‐Principle Study
Bibliographic record
Abstract
OBJECTIVES: A 3-year proof-of-principle study was initiated to evaluate the clinical efficacy of immediately loading titanium dental implants with surfaces enhanced with porous tantalum trabecular metal (PTTM). First-year interim results are presented. MATERIALS AND METHODS: Healthy, partially edentulous patients (n = 30) were enrolled and treated per protocol (minimum insertion torque: ≥35 Ncm) with 37 implants placed in one or two premolar or molar locations in either jaw (study group). Implants were immediately provisionalized out of occlusion with single acrylic crowns. After 7 to 14 days of soft tissue healing, implants were definitively restored in occlusion with ceramometal crowns. Because most study group implants (54.1%, n = 20) had less than 1 year of clinical follow-up, this interim analysis was limited to the first 22 consecutively placed implants in 17 subjects (10 women and 7 men) who completed 1 year of clinical follow-up to date (focus group). RESULTS: To date, one implant failed to integrate in the study group (survival = 97.3%, n = 36/37). Focus group implants achieved 100% (n = 22/22) survival with 0.43 ± 0.41 mm of mean marginal bone loss. There were no serious complications. CONCLUSION: Early clinical findings indicated that immediate loading of PTTM implants was safe and effective under the controlled study conditions.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".