Accuracy of Anterior Immediate Versus Delayed Implant Placement With an Autonomous Robotic System: A Retrospective Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to compare the accuracy of an autonomous robotic system for anterior immediate and delayed implant placement. METHODS: This retrospective study included patients who underwent anterior dental implant surgery from September 2022 to March 2025 using an autonomous robotic system. Osteotomies performed with the autonomous robotic system in this study were modified by using precision and side-cutting drills, along with repeated lifting of drills to minimize deviations. Linear and angular deviations in two- and three-dimensional space were assessed by matching preoperative planning with postoperative cone beam computed tomography. Postoperative pain and surgical satisfaction were recorded using a 100-mm visual analog scale. The Shapiro-Wilk test, Student's t-test, Mann-Whitney U-test, Pearson's Chi-Square test, and Fisher's exact test were used, and p < 0.05 was considered statistically significant. RESULTS: This retrospective study included 53 patients (65 implants) who underwent anterior dental implant surgery with the autonomous robotic system (immediate implant placement group: 19 patients, 21 implants; delayed implant placement group: 34 patients, 44 implants). Comparison of deviations in immediate and delayed implantation using the autonomous robotic system showed a mean (± SD) coronal deviation of 0.57 ± 0.19 mm versus 0.49 ± 0.20 mm (p = 0.129), a mean apical deviation of 0.57 ± 0.19 mm versus 0.52 ± 0.21 mm (p = 0.373), and a mean angular deviation of 0.53° ± 0.18° versus 0.61° ± 0.28° (p = 0.742). Postoperative pain and surgical satisfaction were not significantly different between the two groups (p > 0.05). CONCLUSIONS: The autonomous robotic system demonstrated high and comparable accuracy in both anterior immediate and delayed implantation, with overall patient satisfaction. This could reduce the technical sensitivity of anterior immediate implant placement, enhance precision, and broaden the clinical applications of the autonomous robotic system.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".