Marginal soft tissue stability around conical abutments inserted with the one abutment‐one time protocol after 5 years of prosthetic loading
Bibliographic record
Abstract
BACKGROUND: Soft tissue stability is crucial to obtain and maintain optimal esthetic results. PURPOSE: This study aimed to investigate, over 5 years, the soft tissue response using a conical abutment together with the "one-abutment one-time" (OA-OT) protocol in the restoration of implants inserted in the anterior esthetic area. MATERIAL AND METHODS: From January 2011 to January 2012, all consecutive patients requiring an implant n the maxillary area between canines were enrolled. After submerged healing and osseointegration, a definitive abutment with a provisional crown was inserted. After 1 month, the definitive crown was delivered (Tdef). Analog impressions were taken before tooth extraction (T0), at implant insertion Timpl, and Tdef, and at 12 months (T1) and 60 months (T5). Casts were scanned and superimposed using a dedicated software. Differences in vertical height of soft tissue margins between the digitized model casts were calculated and paired sample t test was conducted to compare results. To detect the potential role of biotype, groups (thick vs. thin) were compared by analysis of variance with general linear model. RESULTS: Twenty-five patients were enrolled. Three patients dropped out. At the 60-month, 22 patients (12 men and 10 women with mean age of 68.3 ± 11 years) concluded the study follow-up. Horizontal changes demonstrated gain of 1.06 mm at Timpl, 0.94 mm at Tdef, 0.92 mm at T1 and 0.97 mm at T5 compared to T0. Vertical changes demonstrated gain of 0.84 mm at Timpl, 0.11 mm at Tdef, 0.29 mm at T1 and 0.59 mm at T5 compared to T0. The analysis of variance showed a significant better performance of thick biotype in soft tissue horizontal width (P = .022). No statistical differences were noticed for vertical width (P = .111). CONCLUSIONS: The use of a conical abutment together with the OA-OT approach allowed longitudinal stable soft tissue dimensions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".