Performance of indirect technique in peri‐implant soft tissue contour duplication in the anterior maxilla: A clinical pilot study
Bibliographic record
Abstract
Abstract Objective To evaluate the performance of the indirect technique in peri‐implant soft tissue contour duplication after the delivery procedure in the anterior maxilla. Materials and Methods Patients with single implant‐supported fixed restorations in the anterior maxilla were recruited. For the impression procedure, an intraoral scan was acquired by both the direct and the indirect techniques. For the delivery procedure, implants were randomly allocated into one of the two groups according to the approaches of digital impression preceding definite crown fabrication (A—direct technique; B—indirect technique) and were scanned again after the definite crown delivery. The stereolithography files were superimposed to analyze changes in peri‐implant soft tissue contour after the delivery procedure. The main outcomes were dimensional deviations of peri‐implant mucosa, and the secondary outcome was differences in the pink esthetic score (PES). Results A total of 20 implants that underwent the complete workflow were included. After the delivery procedure, significant deviations in palatal tissue thickness between the provisional and definite crowns were observed in Group A but these were absent in Group B. Additionally, deviations in labial thickness (0.27 ± 0.12 mm vs. 0.08 ± 0.09 mm) and palatal thickness (0.17 ± 0.15 mm vs. 0.03 ± 0.08 mm), and labial volume of soft tissue (1.87 ± 0.94 mm3 vs. 0.75 ± 0.74 mm3) in Group A were significantly higher than those in Group B. No significant differences in PES were found. Conclusion The indirect technique of scanning the provisional crown can more accurately duplicate the peri‐implant soft tissue contour than the direct technique, resulting in a smaller deviation of the soft tissue in the delivery procedure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".