Accuracy of dynamic computer-assisted implant surgery across different degrees of edentulism: does experience matter? An in vitro study
Bibliographic record
Abstract
PURPOSE: To evaluate the accuracy of a dynamic computer-assisted implant surgery (dCAIS) system utilized by two clinicians with differing levels of surgical experience in implant dentistry. METHODS: A total of 72 conical dental implants were placed into 18 jaw models using a dCAIS system (Navident, ClaroNav, Toronto, Canada). Implant planning and placement were performed by both a novice and an experienced implantologist, following a standardized protocol across single-tooth gaps, shortened dental arches, and fully edentulous jaws. Pre- and postoperative CBCT scans were aligned, and deviations were analyzed at the coronal entry point (2D), apex (3D), apex vertically (V) and in angular deviation (°). Level of significance was set at p < 0.05. RESULTS: Mean deviations across all implants placed were: 1.25 ± 0.90 mm at entry point (2D), 1.66 ± 0.68 mm at apex (3D), 0.70 ± 0.51 mm at apex (V) and 4.96 ± 3.16° angular. No statistically significant differences were found between clinicians, jaws, or sides (p > 0.05). However, a significant correlation was observed regarding accuracy at entry points and angular deviations, indicating lower accuracy in fully edentulous jaws (p < 0.05). CONCLUSIONS: The investigated dCAIS system enabled accurate transfer of the planned to the actual (in vitro) implant position independent of operator experience. dCAIS systems may help mitigate the impact of limited surgical experience and contribute to more predictable outcomes. However, reduced anatomical reference points in edentulous jaws justify further clinical investigation and workflow optimization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".