Soft tissue augmentation at immediate implants using a novel xenogeneic collagen matrix in conjunction with immediate provisional restorations: A prospective case series
Bibliographic record
Abstract
Abstract Background Collagen matrices used around immediate implants may reduce morbidity although there is limited evidence on their performance. Purpose To evaluate soft and hard tissue changes when combining immediate implants, hard and soft tissue grafting, and an immediate provisional restoration. Material and Methods In 12 patients, immediate implants were placed in the anterior maxillary and first premolar area together with a xenogeneic bone substitute. Then a xenogeneic collagen matrix was placed under the buccal mucosal margin with an immediate provisional restoration. Study casts and clinical measurements were taken before extraction (Baseline/BS) at 6 months (6M) and 12 months (1Y) after implant placement. Files from the scanned casts were matched to calculate the linear and volumetric changes at the buccal tissues. Cone Bean Computed Tomographies (CBCTs) were taken prior to extraction and at 6M. The superimposed DICOM files allowed for assessing hard tissue changes and the superimposition of DICOM and STL files allowed for evaluating of soft tissue thickness at BS and 6M. Results After 6 months, the horizontal tissue contours decreased 0.66 ± 0.57 mm, concomitant with a horizontal bone loss of 1.31 ± 1.32 mm, measured 1 mm below the most coronal aspect of the ridge. In contrast, the soft tissue thickness, 1 mm below the gingival margin, increased 0.75 ± 1.12 mm. At 1‐year, tissue contours had decreased 1.01 ± 0.67 mm compared to BS reaching statistical significance. The mean volume loss after 1Y was 20.43 ± 11.70 mm3 while the mean mucosal margin recession was 0.86 ± 0.67 mm. These changes had a limited clinical impact as the PES Scores remained stable. Conclusions The tested protocol resulted in a significant reduction of the tissue contours and osseous ridge dimensions that was partially compensated by a non‐significant increase in soft tissue thickness.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".