Esthetic Evaluation of Implant Crowns and Peri‐Implant Soft Tissue in the Anterior Maxilla: Comparison and Reproducibility of Three Different Indices
Bibliographic record
Abstract
BACKGROUND: A successful implant reconstruction with optimal esthetics consists of a visually pleasing prosthesis and complete and healthy surrounding soft tissue. In the current literature, numerous indices used to qualitatively assess esthetics have been described. However, studies comparing the indices and their reproducibility are scarce. PURPOSE: The aim of this study was to compare three different esthetic indices for the evaluation of single implant-supported crowns. MATERIALS AND METHODS: A total of 10 prosthodontists (P), 10 orthodontists (O), 10 general dentists (G), and 10 lay people (L) independently performed the same assessment using 30 photographs and corresponding casts with three different esthetic indices (Peri-Implant and Crown Index [PICI], Implant Crown Aesthetic Index [ICAI], "Pink Esthetic Score/White Esthetic Score [PES/WES]) and repeated the evaluations 4 weeks later. RESULTS: The PES/WES and the PICI showed significantly higher esthetic scores (pink, white, total) and clinical acceptance compared with the ICAI in all four groups and in both assessments. The highest intraobserver agreement was achieved using the PES/WES and the least with the ICAI. The mean Kappa per group ranged from 0.18 (group L with ICAI) to 0.63 (group G with PICI). CONCLUSION: In comparison with the ICAI, the PES/WES and PICI were more reproducible. Therefore, PES/WES and PICI seem to be more suitable as esthetic indices for single implant crowns.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".