Esthetics of Flapless Single‐Tooth Implants in the Anterior Maxilla Using Guided Surgery: Association of Three‐Dimensional Accuracy and Pink Esthetic Score
Bibliographic record
Abstract
BACKGROUND: Flapless implant placement using surgical templates may guarantee predictable and esthetic results provided that preplanned implant positions are transfered precisely into surgical reality. PURPOSE: The study aims to investigate the effect of three-dimensional accuracy in guided implant surgery on the esthetic outcome of single-tooth implants in the anterior maxilla. MATERIALS AND METHODS: Single-tooth implants for delayed replacement of upper incisors were inserted in 27 patients using stereolithographic templates. Superimposition of postoperative cone beam computed tomography (CBCT) scans allowed assessment of positional inaccuracy at the implant shoulder and apex, as well as angular deviation. Objective evaluation of implant esthetics was performed after a mean follow-up of 2.3 years using the Pink Esthetic Score (PES). RESULTS: Mean deviation between planned and actual implant position was 0.84 mm at the implant shoulder and significantly correlated to average PES of 12 (p = .031). Inaccuracy toward the buccal side was most frequent (70%). Deviations ≥ 0.8 mm resulted in significantly worse implant esthetics (median PES: 9.5, interquartile range [IQR]: 8-11) compared with more accurate implant positions (median PES: 13, IQR: 12-13, p = .039). CONCLUSION: Positional inaccuracy is low in guided implant surgery, but may however significantly compromise implant esthetics in the anterior maxilla.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".