Immediate Implants in Extraction Sockets with Deficient Buccal Walls in the Maxillary Aesthetic Zone
Bibliographic record
Abstract
Background: Immediate implant placement in fresh extraction sockets has become an accepted treatment in dentistry as a predictable procedure to restore failing teeth. One prerequisite for this immediate procedure in the anterior maxillary region is an intact facial wall. Unfortunately, the presence of fenestrations and dehiscences is very common. These defects occur due to the pathology responsible for the extraction of the teeth. Traditionally, hard and soft tissue grafting is necessary to repair these large bony defects before implant placement. However, there are many defects with facial wall deficiencies. Methods: This report reflects procedures used to provide successful functional outcomes using grafting techniques in conjunction with immediate implant placement in defective sockets. This clinical research study followed a qualitative methodology, and the results are based on observational outcomes of four patient surgical implant procedures. Each patient received the same protocol in an attempt to reach similar results. Results: Proper diagnosis, treatment planning, and clinical skills are key factors in achieving predictable results. With each of these four patients, the clinical soft tissue outcomes revealed that the midfacial gingival margin had minimal or no recession at two years with minimal pocket depths less than 3 mm. Conclusions: Although the procedure presented in this article has yet to be clinically validated, it is an available technique that can be used in the hands of an experienced practitioner and can provide excellent results for the patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".