Aesthetic evaluation of orthodontic management of missing maxillary lateral incisors
Bibliographic record
Abstract
BACKGROUND: The most common maxillary lateral incisor agenesis (MLIA) orthodontic management options are space closure or space reopening and implant placement. This study aimed to 1. Examine and compare the aesthetic evaluation patterns of orthodontists, dentists, and laypeople for ``good'' and ``bad'' cases of MLIA treated with space closure, space reopening, and implant placement; 2. Determine if ``good'' and ``bad'' cases were scored differently within each group; and 3. Investigate potential gender differences in aesthetic evaluations. METHODS: A questionnaire was completed by 57 orthodontists, 75 dentists, and 85 laypeople. Participants ranked images of treated MLIA cases on a 1 to 10 scale and identified the best and worst outcomes. Nonparametric statistical analyses (Kruskal-Wallis, Friedman, and Mann-Whitney U tests) were employed for both descriptive and inferential analyses. RESULTS: Laypeople assigned statistically significantly higher ratings to ``bad'' images of both treatment modalities than orthodontists and dentists. Significant differences were found in the comparisons of `good' and `bad' images for both treatment types within the orthodontist and dentist groups. However, statistically significant differences were observed only among laypersons for space opening. Statistically significant sex differences were found among laypeople scoring ``bad'' cases for both treatment approaches. CONCLUSIONS: Dentists and orthodontists can differentiate between good and bad results of closed and open treatments, but laypeople only do so in open cases. Laypeople tend to assign better scores than orthodontists and dentists, and when they are men, they are less critical than women of unesthetic results.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".