Predicting bone and soft tissue alterations of immediate implant sites in the esthetic zone using clinical parameters
Bibliographic record
Abstract
BACKGROUND: Immediate implantation is generally a predictable treatment, but sometimes there are significant tissue alterations at the surgical site which compromise clinical outcomes. PURPOSE: This study aimed to investigate the association between tissue alterations and different clinical parameters in esthetic areas following immediate implant placement and provisionalization. MATERIALS AND METHODS: Clinical parameters were measured at 36 non-grafted immediate implant sites enrolled in a randomized controlled trial. Alterations of bone and soft tissue were measured at 12 months after the treatment. Stepwise linear regression analysis was performed to analyze the association between different clinical parameters and outcomes of interest. RESULTS: Gingival thickness 3 mm apical to the gingival margin (GT3) was positively associated with recession of mid-buccal gingival margin, while vertical distance between the buccal gingival margin and the crest (GM-bone) was negatively associated (P = .03, .01). Flap elevation and older age were positively associated with recession of the interproximal gingival margin (P = .04, .01). Horizontal defect dimension was positively associated with buccal ridge dimensional reduction while gingival thickness at free gingival margin (GT1) was negatively associated (P = .01, .04). Regarding interproximal bone level change, none of the clinical parameters was significantly associated. CONCLUSIONS: Gingival phenotype was the only parameter significantly associated with both buccal gingival recession and buccal ridge dimensional reduction. It is important to assess clinical parameters before and during immediate implant procedure.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".