Immediate Implant Placement and Provisionalization in the Aesthetic Zone Using a Digital Workflow: A 1‐Year Prospective Case Series Study
Bibliographic record
Abstract
INTRODUCTION: Immediate implant placement and provisionalization in the maxillary aesthetic zone necessitates meticulous treatment planning. The integration of intraoral scanning with cone beam computed tomography allows for three-dimensional prosthetic-driven planning of the implant. Additionally, it facilitates static computer-assisted implant surgery and prefabrication of a temporary restoration, enabling chairside restoration of the immediate implant. This study aimed to evaluate the clinical, aesthetic, radiographic, and patient-reported outcomes after immediate implant placement and restoration. MATERIALS AND METHODS: In a prospective case series, 30 patients with a failing tooth in the maxillary aesthetic zone were included and received immediate implant placement with a bone graft and a prefabricated temporary restoration. The definitive restoration was placed 3 months later. The clinical, aesthetic, radiographic, and patient-reported outcomes were collected prior to implant treatment, 6 weeks after the temporary restoration, and 1 month and 1 year after the definitive restoration. RESULTS: The prefabricated temporary restoration could be placed in all patients. Three cases of early implant failure were observed (implant survival rate 90%); hence, 27 patients were evaluated after 1 year. The survival and success rates were 100% for the temporary and 100% and 96%, respectively, for the definitive restorations. Plaque, bleeding on probing, and peri-implant inflammation were absent in most cases. At the 1-year follow-up, the mean (SD) Pink Esthetic Score and White Esthetic Score (scale 0-20) was 15.4 (2.5). The mean (SD) marginal bone level change between implant placement and the 1-year follow-up was -0.18 mm (0.57) on the mesial side and -0.44 mm (1.23) on the distal side. The median buccal bone thickness remained stable after immediate implant placement and grafting. The mean (SD) patient satisfaction (scale 0-10) was 9.2 (0.8) at the 1-year evaluation. CONCLUSION: The digital workflow has the potential to enable the manufacture of prefabricated temporary restorations, leading to satisfactory clinical, aesthetic, radiographic, and patient-reported outcomes after immediate placement of single-tooth implants in the aesthetic zone. CLINICAL TRIAL REGISTRATION: Registered in the National Trial Register (NL8264).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".