Influence of soft tissue grafting, orofacial implant position, and angulation on facial hard and soft tissue thickness at immediately inserted and provisionalized implants in the anterior maxilla
Bibliographic record
Abstract
BACKGROUND: Resorption of hard and soft tissues following immediate implant insertion is frequently reported. Data regarding the influencing factors on facial tissue thickness are rare. PURPOSE: This retrospective study investigated the impact of connective tissue grafting, the orofacial angulation and position of immediately inserted and provisionalized implants on the facial hard and soft tissue thickness in the anterior maxilla within a 1- to 5-year follow-up. MATERIAL AND METHODS: Implants with the prerequisite of having preoperative and postoperative cone beam computed tomography (CBCT) and a follow-up of 1 to 5 years were included. Facial bone deficiencies were grafted flaplessly with autogenous bone in all sites. In a subgroup of implants additional connective tissue grafting was performed, whereas the remaining implants were not grafted with soft tissue. The orofacial tooth and implant angulation, the change of horizontal position and the facial bone thickness were measured by CBCT, the facial mucosa thickness by an ultrasonic device. RESULTS: In total, 76 implants were placed in 55 patients. Sixty-nine sites showed a facial bone defect. Thirty-eight received a connective tissue graft additionally. All implants were still in function after a mean follow-up of 36 months. The mean thickness of the facial mucosa was 1.72 mm at 1 mm, 1.63 mm at 4 mm, 1.52 mm at 6 mm, and 1.66 mm at 9 mm apically to mucosal margin. The bone thickness was 0.02, 0.25, and 0.36 mm initially and 1.32, 1.26, and 1.11 mm finally at 1, 3, and 6 mm apically to implant shoulder level. Mixed model analysis revealed an impact of the preoperative bone status on the facial bone increase. The facial soft tissue thickness was significantly influenced by the gingival biotype. CONCLUSIONS: The results indicate that an initial severe hard tissue defect allows for significant bone regeneration. The facial soft tissue thickness is primarily influenced by the gingival biotype.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".