Accuracy of the Yakebot dental implant robotic system versus fully guided static computer‐assisted implant surgery template in edentulous jaw implantation: A preliminary clinical study
Bibliographic record
Abstract
AIMS: To compare the accuracy of the Yakebot dental implant robotic system with that of fully guided static computer-assisted implant surgery (CAIS) template in edentulous implantation. MATERIALS AND METHODS: Thirteen patients with edentulous were recruited and divided into two groups: the Yake robotic system group (experimental) (n = 5) and the CAIS group (control) (n = 8). Postoperative cone-beam computed tomography (CBCT) was performed immediately, and the 3-dimensional positions of implants were obtained and compared with that in the preoperative design. The comparison showed platform, apical, depth, and angular deviations. A value of p < 0.05 was considered statistically significant. RESULTS: A total of 84 implants (36 in the robotic group and 48 in the CAIS group) were placed. The mean deviation at the implant platform, apex, depth, and angle in the CAIS group was 1.37 ± 0.72 mm, 1.28 ± 0.68 mm, 0.88 ± 0.47 mm, and 3.47 ± 2.02°, respectively. However, the mean deviation at the implant platform, apex, depth, and angle in the robotic group was 0.65 ± 0.25 mm, 0.65 ± 0.22 mm, 0.49 ± 0.24 mm, and 1.43 ± 1.18°, respectively. Significant differences in the four types of deviation (p < 0.05) between the two groups were observed. CONCLUSION: The accuracy of robotic system in edentulous implant placement was superior to that of the CAIS template, suggesting that robotic system is more accurate, safe, and flexible, can be considered a promising treatment in clinical practice.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".