Standard vs customized healing abutments with simultaneous bone grafting for tissue changes around immediate implants. 1‐year outcomes from a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Immediate implants have shown risks of esthetic complications. The hypothesis was that a customized healing abutment could improve the peri-implant tissue healing. PURPOSE: To evaluate and compare the soft and hard tissue healing around immediate implants that received bone grafting and a customized vs a standard healing abutment. MATERIALS AND METHODS: Patients, who required tooth extraction and who received an immediate implant (with an alloplastic graft material) were randomly assigned to a customized or a standard healing abutment group. Clinical and radiographic examinations were taken at baseline, at 4 and 12 months. RESULTS: Twenty-five patients out of 61 were excluded from the study because unsuitable for immediate implantation. In total, 36 patients were randomized in the two groups. There were 17 females and 19 males (age range 23-77). No prosthetic or implant failure was registered during the study period. The Papilla Index was significantly higher in the customized than in the standard group at 4 and 12 months (P = .0002). The bone loss at mesial sites was significantly higher in the control than in the test group (P = .0014). CONCLUSION: The customized healing abutment group showed the most favorable outcomes (in terms of PI and MBL) in case of immediate implant that received a peri-implant bone grafting procedure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".