Immediate Implant Placement in the Esthetic Zone: A Multi‐Variate Analysis of Influencing Factors
Bibliographic record
Abstract
OBJECTIVES: To evaluate the dimensional reduction of the peri-implant hard tissues and esthetic outcome after immediate implant placement (IIP) in the esthetic zone. MATERIAL AND METHODS: Patients who received IIP with bone grafts in the esthetic zone, with either immediate or delayed restoration, were included in this retrospective cohort study. The implants were categorized into three groups based on the labial bone thickness (LBT) before implantation: Group 1 (≤ 0.5 mm), Group 2 (0.5-1 mm), Group 3 (≥ 1 mm). The horizontal bone loss (HBL) at 0, 3, 5 mm apical to implant shoulder, peri-implant marginal bone loss (MBL), and Pink Esthetic Score (PES) were used to evaluate the hard and soft tissue after IIP. RESULTS: A total of 87 implants in 74 patients met the inclusion criteria. Compared to group 3, there was significant severe bone loss in the HBL-0 mm in groups 1 (p = 0.017); and the implant located in the central incisor position and female may led to increased bone resorption (p = 0.021, p = 0.061, respectively). For HBL-3 mm and HBL-5 mm, the non-immediate restoration may reduce bone resorption (p = 0.013, p = 0.022, respectively). The MBL during short-term follow-up and PES score showed no significant difference among three groups. CONCLUSIONS: Despite limitations, our study found that LBT < 0.5 mm significantly affected horizontal bone loss. Meanwhile, immediate restoration, implant position of central incisors and female may also be considered as risk factors for HBL. However, the difference in the labial bone did not significantly affect MBL, or peri-implant soft tissue outcomes. TRIAL REGISTRATION: This study was registered in a clinical trial registry (www.chictr.org.cn, No: ChiCTR2400087990).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".