The efficacy of immediate implant placement in the anterior maxilla with dehiscence in the facial alveolar bone: A case series
Bibliographic record
Abstract
BACKGROUND: There is no clear evidence that immediate implant placement can be applied to cases with dehiscence in the facial alveolar bone prior to extraction. PURPOSE: To evaluate the results of immediate implant placement in the anterior maxilla with facial alveolar bone dehiscence. MATERIALS AND METHODS: We super positioned pre- and post-operative cone-beam computed tomography (CBCT) three-dimensional reconstruction images. A CBCT was taken before tooth extraction (T0), when the definitive restoration was placed (T1), and 1 year after placing the definitive restoration (T2). The depth and width of the dehiscence at T0, and the height and width of the facial hard and soft tissues are measured at the implant site at T1 and T2. We calculated the change in the amount of hard and soft tissues from T1 to T2 and determined the correlation between preoperative facial alveolar bone morphology and postoperative gingival recession. RESULTS: 13 women and 7 men were recruited. A total of 20 implants were evaluated. The implant survival rate was 100%. The mean facial alveolar bone dehiscence width was 3.9 ± 1.6 mm, and the mean depth from platform level was 2.9 ± 1.7 mm. The mean implant body exposure on the buccal was 4.8 ± 1.7 mm, and the mean socket width gap was 2.1 ± 0.8 mm. At T1, the mean facial hard tissue width was 2.1 ± 0.7 mm, and the mean height was 2.0 ± 0.7 mm. The mean change in vertical gingival recession from T1 to T2 was 0.5 ± 0.5 mm. We found a positive correlation between facial alveolar bone dehiscence width and gingival recession (r = 0.46, p-value = 0.04) and between dehiscence depth and gingival recession (r = 0.48, p-value = 0.03). CONCLUSIONS: The results of our CBCT superposition method indicated that immediate implant placement can be considered in patients with facial alveolar bone dehiscence. However, there may be a higher risk of gingival recession with wide or deep dehiscence.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".