Labial soft tissue contour dynamics following immediate implants and immediate provisionalization of single maxillary incisors: A 1‐year prospective study
Bibliographic record
Abstract
Abstract Background Soft tissue dynamics in the esthetic zone are gaining increasing attention in recent years. Emerging intraoral scanning technology allows easier capture of soft tissue contours. Purpose To quantitatively assess the time‐dependent contour alterations of labial soft tissue following single immediate implants and immediate provisionalization (IIPP) in maxillary incisors via intraoral scanning. Materials and Methods This was a prospective cohort study. Thirty eligible consecutive patients were included and received immediate replacement of a failure maxillary single incisor. A screw‐retained immediate restoration was delivered for each patient. Subsequently, the anterior maxillary region was scanned by an intraoral scanning system at four time points: preoperation (baseline, BL), 3 months (3 m), 6 months (6 m), and 12 months (12 m). The Standard Tessellation Language files were exported to a dedicated software and superimposed for visual analysis. At 3, 6, and 12 months, the mid‐facial mucosa level (ML) was assessed, and the precise three‐dimensional (3D) configuration of the altered volume was calculated and reconstructed for visual analysis. Furthermore, quantitative analysis of the reconstructed morphology was performed using the following parameters: mean change in thickness (△ d ), mesio‐distal width ( w ), coronal‐apical height ( h ), and horizontal and vertical position of the thickest point represented by coordinates ( x , z ). Result Twenty‐seven of thirty enrolled patients were finally available for analysis at the 1‐year follow‐up. In general, the frontal view of the reconstructed volume exhibited a crescent shape. The mid‐facial ML change at 3, 6, and 12 months was −0.05 ± 0.36 mm, −0.03 ± 0.32 mm, and −0.24 ± 0.37 mm, respectively ( P = .012). The mean change in thickness at 3 months (△ d 3m ), 6 months (△ d 6m ), and 12 months (△ d 12m ) was 0.50 ± 0.19 mm, 0.59 ± 0.21 mm, and 0.62 ± 0.22 mm, respectively ( P <.001). At 12 months, nine patients had a △ d less than 0.5 mm. The mean △ d 3 m /△ d 12 m and △ d 6 m /△ d 12 m was 0.81 ± 0.17 and 0.96 ± 0.13. The w , h , x , and z results showed no significant differences during the 1‐year observation ( P = .126, P = .324, P = .635, P = .263). At 12 months, w , h , x , and z were 11.57 ± 1.77 mm, 6.46 ± 2.01 mm, 0.03 ± 1.43 mm, and 2.16 ± 0.65 mm, respectively. Conclusion During the 1‐year observation following single IIPP treatment in maxillary incisors, the labial soft tissue contour showed a continuous alteration resulting in a mean change in thickness of 0.62 mm that occurred mainly in the first 3 months and tended to be relatively stable after 6 months, while the crescent‐like shape, width, height, and thickest point position of the alteration volume remained stable after 3 months. No advanced mid‐facial recession was observed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".