Accuracy of Robotic Computer‐Assisted Implant Surgery for Transcrestal Sinus Floor Elevation: A Retrospective Case Series Study
Bibliographic record
Abstract
AIM: To investigate the accuracy of robotic computer-assisted implant surgery (r-CAIS) for transcrestal sinus floor elevation (TSFE) with simultaneous implant placement. MATERIALS AND METHODS: Virtual sinus elevation and a stepwise drilling plan were created on the robotic operating system before surgery. Robotic arm automatically executed drilling tasks during procedure. Fourteen implants in ten patients with missing teeth in the posterior maxilla were placed by robotic computer-assisted implant surgery through TSFE. Deviations between the planned and placed implants were evaluated with an immediate postoperative CBCT scan. The coronal, apical, and angular deviations between the planned and actual implant placement were measured. RESULTS: A total of 10 patients with edentulism in the posterior maxilla were included, and 14 implants were placed. The robot-assisted TSFE with simultaneous implant surgery exhibited a mean global coronal deviation of 0.72 mm (range: 0.32-1.57 mm, 95% CI: 0.52-0.92 mm), a mean global apical deviation of 0.78 mm (range: 0.33-1.50 mm, 95% CI: 0.60-0.96 mm), and an angular deviation of 2.20° (range: 0.16°-8.70°, 95% CI: 0.82°-3.60°), respectively. Throughout the surgical intervention, no immediate or significant complications were noted, and no evidence of complications such as tissue perforation or premature implantation failure was documented in the postoperative phase. CONCLUSIONS: The r-CAIS-assisted TSFE demonstrated potential techniques for implant osteotomy and placement. Nevertheless, further clinical trials are necessary to reinforce evidence-based clinical outcomes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".