Comparison of Esthetic Outcomes of Maxillary Lateral Incisor Agenesis Treatment by Orthodontic Space Closure Versus Implant Placement (Evaluated by Pink Esthetic Score)
Bibliographic record
Abstract
Introduction: Due to the fundamental role of esthetics in the outcomes of dental treatments, especially in the anterior region (esthetic zone), the necessity of considering the matter of esthetics in clinical studies has become into focus in the current era. The aim of this study was the evaluation of esthetic outcomes of two treatment protocols in the treatment of congenital uni-lateral missing of maxillary lateral incisors as well as patient satisfaction from the treatment outcomes. Methods: in this study the sample size was 24 people (16 women and 8 men), These individuals sought dental treatment for replacement of the congenitally missing maxillary lateral incisor. Convenience sampling method was used and patients were divided into two groups regarding the kind of treatment they received. The two treatment protocols included: 1. Space closure by means of orthodontic treatment and then reshaping the canines; and 2. Space regaining by means of orthodontic treatment and replacing the lateral incisor with dental implants. Photographs of patients were acquired from the frontal view with retraction of the lips using digital cameras. Photographs were evaluated for Pink esthetic score. Results: No significant difference was detected between the two study groups in the evaluated factors in this study. Conclusion: The results of this study indicated that there is no significant difference in esthetic results in the two groups. Furthermore, both groups lead to similar results in patient satisfaction from treatment outcomes.
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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 | 0.000 | 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 | 0.000 |
| 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".