Comparison of the accuracy of implant position for two‐implants supported fixed dental prosthesis using static and dynamic computer‐assisted implant surgery: A randomized controlled clinical trial
Bibliographic record
Abstract
BACKGROUND: Computer-assisted implant surgery (CAIS) can facilitate accuracy of single implant placement, but little is known with regards to parallelism between multiple implants. PURPOSE: To compare the accuracy of position and parallelism of two implants, using static and dynamic CAIS systems. MATERIALS AND METHODS: Thirty patients received two implants (60 implants) randomly allocated to two different CAIS systems. Optimal implant position and absolute parallelism was planned based on preoperative cone beam CT (CBCT). Patients received implants with a surgical guide (static CAIS, n = 30) or real-time navigation (dynamic CAIS, n = 30). Implant three-dimensional deviation and parallelism was calculated after surgery. RESULTS: The mean 3D deviation in the static and dynamic CAIS group at implant platform were 1.04 ± 0.67 vs 1.24 ± 0.39 mm, at apex were 1.54 ± 0.79 vs 1.58 ± 0.56 mm and angulation were 4.08° ± 1.69° vs 3.78° ± 1.84°, respectively. The angle deviations between two placed implants (parallelism) in static and dynamic CAIS groups were 4.32° ± 2.44° and 3.55° ± 2.29°, respectively. There were no statistically significant differences in all parameters between groups. CONCLUSION: Static and dynamic CAIS provides similar accuracy of the 3D implant position and parallelism between two implants.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".