Esthetic Outcome of Implant Supported Crowns With and Without Peri‐Implant Conditioning Using Provisional Fixed Prosthesis: A Randomized Controlled Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Achieving an optimal esthetic result using dental implants is challenging. Fixed implant-supported provisional crowns are often used to customize the emergence profile and to individualize the surrounding peri-implant soft tissue. PURPOSE: The objective of this study is to evaluate whether the use of a provisional implant-supported crown leads to an esthetic benefit on implants that are placed in the esthetic zone. The null hypothesis is that there is no-difference between the two study groups. MATERIAL AND METHODS: Twenty single implants (Bone Level, Straumann AG, Basel, Switzerland) were inserted in consecutive patients. After reopening, a randomization process assigned them to either cohort group 1: a provisional phase with soft tissue conditioning using the "dynamic compression technique" or cohort group 2: without a provisional. Implants were finally restored with an all-ceramic crown. Follow-up examinations were performed at 3 and 12 months including implant success and survival, clinical, and radiographic parameters. RESULTS: After 1 year all implants successfully integrated, mean values of combined modPES and WES were 16.7 for group 1 and 10.5 for Group 2. This was statistically significant. Mean bone loss after 1 year was -0.09 and -0.08 for groups 1 and 2, respectively, without being statistically significant. CONCLUSION: A provisional phase with soft tissue conditioning does improve the final esthetic result.
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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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".