The influence of submerged healing abutment or subcrestal implant placement on soft tissue thickness and crestal bone stability. A 2‐year randomized clinical trial
Bibliographic record
Abstract
PURPOSE: Aims of the study were: (a) to register crestal bone loss around 1.5 mm subcrestally placed implants and epicrestally placed implants with soft tissue tenting technique, (b) to record bone remodeling in subcrestal group, and (c) to determine the increase of vertical soft tissues after tenting. MATERIALS AND METHODS: Thirty-two patients with vertically thin tissues of 2 mm or less received 40 submerged bone level platform-switched implants, divided into two groups-(a) 1.5 mm subcrestally placed implants and (b) epicrestally placed implants with soft tissue tenting over 2 mm healing abutments. At the second stage surgery, implants received 4 mm healing abutments, soft tissue thickness was measured in epicrestal group, and later implants were restored with zirconia-based screw-retained single restorations. Radiological images were taken at the second stage surgery, restoration delivery and after 2 years of follow-up. Bone loss was calculated as a distance between implant neck and first radiographically visible bone-to-implant contact. Bone remodeling was calculated as a distance between the bone crest and implant neck. Mann-Whitney U test was used for statistical analysis, significance set to 0.05. RESULTS: After 2 years of follow-up, Group 1 (subcrestal) had 0.18 ± 0.32 mm of bone loss, Group 2 (epicrestal with 2 mm healing abutment) had 0.51 ± 0.4 mm of bone loss, with statistically significant difference (P = .001). Bone remodeling in Group 1 (subcrestal) was 1.17 ± 0.51 mm. Vertical tissue thickness in epicrestal group before the intervention was 1.85 ± 0.26 and 3.65 ± 0.41 mm after the use of 2 mm healing abutment, with a statistical difference (P = .005). CONCLUSION: Subcrestal implant placement can significantly reduce crestal bone loss, compared to vertical soft tissue thickening by tenting of epicrestally placed implants, although soft tissue tenting can significantly increase 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".