Accuracy of Dynamic Computer-Aided Implant Surgery for Biconometric Implant Positioning: A Retrospective Case Series Analysis
Bibliographic record
Abstract
Background/Objectives: This retrospective study assessed the accuracy of implant positioning with dynamic computer-aided implant surgery (dCAIS) for Toronto Bridge fabrication, using a conometric prosthetic concept and a new intraoral splinting technique (CLIKSS). It compared discrepancies across various anatomical regions, bone qualities, and implant sites. Methods: This study involved 52 patients undergoing full-arch rehabilitation (17 in the mandible, 30 in the maxilla, and 5 in both), with 366 implants placed (125 in the mandible, 241 in the maxilla; 128 in post-extraction sites, and the remainder in healed sites). All implants were immediately loaded. Precision was assessed by measuring linear and three-dimensional (3D) angular deviations between planned and actual implant positions. Results: Measurement errors for apical linear and 3D deviations at the apex and entry point ranged from 0.24 ± 0.10 to 0.55 ± 0.57 mm, and angular deviations varied from 0.32 ± 0.65° to 0.35 ± 0.71°. Maxillary measurements were significantly higher at the entry, apical, and vertical levels, even when comparing anterior and posterior regions with the corresponding mandibular areas, while no differences were found in the angular deviation. Significant discrepancies were observed among different mandibular bone types. Maxillary post-extraction sites exhibited significantly greater deviations than mandibular sites in all parameters except angular deviation. No significant differences were found between healed and post-extraction sites within the same jaw. Conclusions: dCAIS improved implant placement accuracy, leading to predictable prosthetic outcomes, especially during parallel multi-implant insertions. This report introduced dCAIS for conometric/biconometric implant placement combined with the innovative CLIKSS technique as an effective intraoral split method for this prosthesis connection.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".