Preservation of Esthetics with Implant Dentistry: a Clinical Report
Bibliographic record
Abstract
BACKGROUND: The replacement of single teeth with implants in the anterior maxilla poses a significant challenge to the clinician because of its esthetic implications. PURPOSE: The purpose of this article is to report a protocol in which single teeth are replaced by implants and temporarily restored without load, immediately after extraction. MATERIALS AND METHODS: Between June 2001 and July 2002, 16 cases were followed up in which this protocol was used. Briefly, the teeth are extracted with a Periotome (Friadent, Mannheim, Germany), using a flapless technique, the sockets are evaluated and débrided, an osteotomy is performed following the direction of the socket, and a tapered implant is placed using the alveolar crest as the landmark. A temporary abutment is immediately custom made, and a temporary crown is placed without loading. The 16 cases are summarized, and 4 of them are presented to illustrate this approach. RESULTS: All four cases have provided excellent esthetic results maintaining an ideal implant-to-supporting-tissue relationship. Furthermore, the overall time needed to restore the cases was considerably reduced when compared with time needed for the conventional approach. CONCLUSION: The four cases presented support the use of the described protocol in the placement of single implants in areas of the maxillary anterior region where esthetics is a high priority.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".