Does initial buccal crest thickness affect final buccal crest thickness after flapless immediate implant placement and provisionalization: A prospective cone beam computed tomogram cohort study
Bibliographic record
Abstract
BACKGROUND: Flapless immediate implant placement and provisionalization (FIIPP) in the aesthetic zone is still controversial. Especially, an initial buccal crest thickness (BCT) of ≤1 mm is thought to be disruptive for the final buccal crest stability jeopardizing the aesthetic outcome. PURPOSE: To radiographically assess the BCT and buccal crest height (BCH) after 1 year and to calculate the correlation between initial and final achieved BCT. MATERIALS AND METHODS: The study was designed as a prospective study on FIIPP. Only patients were included in whom one maxillary incisor was considered as lost. In six centers, 100 consecutive patients received FIIPP. Implants were placed in a maximal palatal position of the socket, thereby creating a buccal space of at least 2 mm, which was subsequently filled with a bovine bone substitute. Files of preoperative (T0), peroperative (T1) and 1-year postoperative (T3) cone beam computed tomogram (CBCT) scans were imported into the Maxillim™ software to analyze the changes in BCT-BCH over time. RESULTS: Preoperatively, 85% of the cases showed a BCT ≤1 mm, in 25% of the patients also a small buccal defect (≤5 mm) was present. Mean BCT at the level of the implant-shoulder increased from 0.6 mm at baseline to 3.3 mm immediate postoperatively and compacted to 2.4 mm after 1 year. Mean BCH improved from 0.7 to 3.1 mm peroperatively, and resorbed to 1.7 mm after 1 year. The Pearson correlation of 0.38 between initial and final BCT was significant (p = 0.01) and therefore is valued as moderate. If only patients (75%) with an intact alveolus were included in the analysis, still a "moderate correlation" of 0.32 (p = 0.01) was calculated. CONCLUSIONS: A "moderate correlation" was shown for the hypothesis that "thinner preoperative BCT's deliver thinner BCT's" 1 year after performing FIIPP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".