Soft Tissue Augmentation in Connection to Dental Implant Treatment Using a Synthetic, Porous Material – A Case Series with a 6‐Month Follow‐Up
Bibliographic record
Abstract
BACKGROUND: Bony defects/concavities in the aesthetic zone of maxillae may interfere with the results of prosthetic procedures by producing shading superior to the crown. Such regions can be augmented either by bone or soft tissue autografts, allografts, or xenografts. Tissue shrinkage is thus anticipated, and a method to objectively measure the tissue change is valuable. PURPOSE: The aim of this study was to evaluate the use of a synthetic, porous material made of polyurethaneurea for buccal soft tissue augmentation in connection with implant placement in the maxillary front region. Further, to measure over time the change in buccal contour using a computerized technique. MATERIALS AND METHODS: Ten patients received 12 Artelon® cylinders (5 × 10 mm) in connection to implant placement. Preoperative and postoperative (at 3 and 6 months) study casts were obtained for computer measurements, using the preoperative reference model as a base. The volume created between the surfaces of the reference model and each of the two following superimposed models was measured in cubic millimeter. Differences in volume from pretreatment to 3 and 6 months, respectively, were compared. RESULTS: The clinical observation during follow-up showed normal healing. The increase in mean buccal tissue volume was 50 mm(3) (SD 18) after 3 months and 43 mm(3) (SD 21) after 6 months, measured over a 6 mm × 8 mm area in the maxillary front region, in comparison to before insertion of the cylinder. The reduction from 3 to 6 months was not statistically significant (p = .17). CONCLUSION: A synthetic, porous material for soft tissue augmentation was tested in connection to implant placement in the aesthetic zone of maxillae. The buccal contour was followed-up for 6 months using a computer volumetric technique on preoperative and postoperative study casts. Measured tissue volume showed an obvious increase during the study period. The material was biologically well received.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".