The thickness of labial bone affects the esthetics of immediate implant placement and provisionalization in the esthetic zone: A prospective cohort study
Bibliographic record
Abstract
BACKGROUND: A 1-2 mm thick labial plate is generally advocated in immediate implant placement and provisionalization (IIPP). However, most of the human labial plates fail to meet this requirement. PURPOSE: This study aimed to investigate the effect of labial plate thickness on hard tissue, soft tissue, and esthetic outcomes in IIPP. MATERIALS AND METHODS: In this prospective cohort study, 40 patients received IIPP of 50 single-crown implants in the anterior maxilla. Patients were categorized into three groups according to their presurgical thickness of labial bone: 0-0.5, 0.5-1, and ≥ 1 mm. CBCT, mucosa recession and the papilla index were used to analyze labial hard and soft tissue alterations with a 1-year follow-up. RESULTS: At 1 year, OCI bone losses were 1.17 ± 0.73, 0.37 ± 0.39, 0.46 ± 0.35 mm; ICH bone losses were 2.23 ± 1.83, 0.74 ± 0.71, 0.72 ± 1.27 mm; TM recessions were 1.00 ± 0.51, -0.06 ± 0.37, -0.30 ± 0.88 mm; TL recessions were 0.61 ± 1.02, -0.18 ± 0.40, -0.26 ± 1.15 mm; TD recessions were 0.61 ± 1.02, -0.18 ± 0.40, -0.26 ± 1.15 mm; PIS scores were 1.63 ± 0.64, 2.20 ± 0.71, 2.71 ± 0.57 in group 0-0.5, 0.5-1 and ≥ 1 mm, respectively. No statistical significance was found between group 0.5-1 and ≥ 1 mm in bone resorption, gingival recession, and papilla index. The bone resorption and gingival recession were significantly the highest in group 0-0.5 mm at 6 months and 1 year. CONCLUSIONS: Group 0.5-1 mm had similar tissue dimensional alteration as group ≥1 mm, while group <0.5 mm suffered more massive bone resorption and gingival recession. Concerning the thickness of the labial plate, this study may suggest an expansion in the indication of IIPP.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".