Accuracy of Robotic Vesus Fully Guided Static Computer‐Assisted Implant Surgery With Transcrestal Sinus Floor Elevation
Bibliographic record
Abstract
OBJECTIVES: To evaluate the accuracy of dental implant placement using fully guided static computer-assisted implant surgery (s-CAIS) and autonomous robotic computer-assisted implant surgery (r-CAIS) technology in patients with transcrestal sinus floor elevation. MATERIALS AND METHODS: Patients with posterior teeth loss and transcrestal sinus floor elevation using s-CAIS or r-CAIS technology for implant surgery were included in this study. A total of 34 patients with 42 implants were included in the study (17 patients with 19 implants in the autonomous r-CAIS group, 17 patients with 23 implants in the fully guided s-CAIS group). Postoperative cone-beam computed tomography (CBCT) scans were used to determine the discrepancies between the planned and actually placed implants. The preoperative and postoperative CBCT were utilized to estimate the linear deviations and angular deviations in two-dimensional (2D) and three-dimensional (3D) space. RESULTS: A total of 42 implants were included, with significant differences between the autonomous r-CAIS group and fully guided s-CAIS group (p < 0.001). No adverse surgical events occurred. The 3D deviations at the implant platform were 0.484 ± 0.218 mm for the autonomous r-CAIS group and 1.179 ± 0.776 mm for the fully guided s-CAIS group, respectively. The mean linear deviations at the implant apex were 0.527 ± 0.247 and 1.196 ± 0.830 mm, respectively. The mean angular deviation was 0.882° ± 0.967° for the autonomous r-CAIS group and 2.478° ± 1.524° for the fully guided s-CAIS group. CONCLUSIONS: Autonomous r-CAIS technology provided a more accurate surgical approach for implant placement in patients with transcrestal sinus floor elevation than fully guided s-CAIS.
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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 | 0.005 |
| 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 | 0.001 | 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".