Buccal bone thickness at dental implants in the maxillary anterior region with large bony defects at time of immediate implant placement: A 1‐year cohort study
Bibliographic record
Abstract
BACKGROUND: There is lack of studies regarding preservation and possible changes in BBT at dental implants. PURPOSE: To assess, on cone beam computer tomograms, the presence of bone at the time of tooth extraction in the maxillary esthetic region and the mean buccal bone thickness 1 month and 1 year after final restoration placement in patients with large bony defects. MATERIAL AND METHODS: In a cohort study, patients were selected presenting a failing tooth with a large bony defect (test group [n = 20]: large bony defect, immediate placed implant and delayed provisionalization). Results were compared with a group in which patients presented a failing tooth without or with a small bony defect: (control group [n = 20]: without or small bony defect, immediate placed implant and delayed provisionalization). Cone beam computer tomograms were made preoperatively, and 1 month and 1 year after placement of the restoration, and buccal bone thickness was analyzed. RESULTS: In both groups approximately 1 mm of buccal bone thickness was present after 1 month and 1 year, without a significant difference between the groups. CONCLUSION: In patients with large bony defects at a failing tooth it was possible to create a bone layer buccally of the implant and this bone layer remained stable during a 1-year follow-up; there were no significant differences between thickness of buccal bone at 1 month and 1 year in patients with large buccal bony defects and patients without or with small bony defects.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".