Accuracy of robotic computer‐assisted implant surgery for immediate implant placement: A retrospective case series study
Bibliographic record
Abstract
OBJECTIVES: This study investigated the accuracy of robotic computer-assisted implant surgery (r-CAIS) for immediate implant placement. METHODS: Twenty cases with 20 implant sites were selected based on predefined inclusion criteria. The preparation of the implant bed and implant placement followed the standardized dental implant robotic surgery protocol. Postoperative cone-beam computed tomography scans were conducted to assess possible discrepancies between actual and planned implant positions. RESULTS: The r-CAIS technology for immediate implant placement exhibited a mean global coronal deviation of 0.71 ± 0.27 mm (95% CI: 0.58-0.83 mm), a mean global apical deviation of 0.69 ± 0.26 mm (95% CI: 0.56-0.81 mm), and an angular deviation of 1.27 ± 0.47° (95% CI: 1.05-1.49°). A substantial number of deviations were observed buccally at both coronal (90%) and apical (95%) levels, with a consistent tendency for buccal deviation. CONCLUSIONS: The r-CAIS technology proved a promising approach for immediate implantation in the anterior region, with satisfactory clinical outcomes. However, an optimized surgical protocol for r-CAIS technology is required for particular implant sites like extraction sockets or bone defects.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".