Influence of buccal emergence profile designs on peri‐implant tissues: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The prosthetic emergence profile design might be an important factor in postsurgical mucosal recession etiology. Therefore, a restorative buccal emergence profile designed correctly might reduce gingival margin recession. PURPOSE: To compare the marginal gingival level and the width/height (W/H) ratio between two profile configurations of single implant-supported restorations at molar sites. MATERIALS AND METHODS: Twenty-one patients requiring a single mandibular molar tooth replacement with supracrestal mucosal thickness ≥2 mm were recruited and randomly assigned to a prosthesis buccal emergence profile design based on the buccal mucosal W/H ratio (Test Group) or maintained the original emergence profile of the healing abutment (Control Group). Assessments were made before delivery of the definitive restoration (T0), at prosthesis placement (T1), one (T2), and 12 (T3) months after loading. The gingival margin level change (△GM), initial emergence angle, buccal mucosal W/H ratio, marginal bone loss (MBL), implant failure, and complications were assessed. RESULTS: The gingival recession in the test group (0.13 ± 0.32 mm) was significantly lower than in the control group (0.63 ± 0.38 mm) at T3 (p = 0.006). The initial emergence angle in the test group (31.4 ± 7.22 degrees) was significantly lower than the control group (40.0 ± 7.60 degrees) (p = 0.025). The W/H ratio in the test group at T2 was significantly higher than at T0 but remained stable thereafter. The W/H ratio presented a continued rising trend in the control group. CONCLUSIONS: When the initial supracrestal soft tissue thickness was ≥2 mm, a restorative emergence profile based on the W/H ratio significantly reduced gingival margin recession. An emergence angle of 32.4 degrees showed better behavior in maintaining the gingival margin than 40 degrees. CLINICAL TRIAL REGISTRATION NUMBER: ChiCTR190002210.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".