Hybrid Navigation Technique for Improved Precision in Implantology
Bibliographic record
Abstract
The hybrid navigation technique involves the merging of the Dynamic navigation (DN) system (Navident, Claronav, Canada) and static navigation technique (3Shape, Copenhagen, Denmark). Combining the advantages of both techniques, devising a protocol of hybrid navigation will be advantageous to eliminate the difficulties faced by operators in using either methods separately. Three patients requiring dental implants were included in this study. This requires the cone beam computed tomography (CBCT) (Digital Imaging and Communications in Medicine (DICOM) data) and intra-oral scan (Standard Tessellation Language (STL) format) data for the accurate planning of the implant positions in both the static and dynamic approaches. The steps carried out were repeated for each of the patients, the accuracy of the implant placement was verified postoperatively by merging the CBCT data pre and post through the Evalunav software (NaviDent, Claronav). The accuracy of the implants placed were assessed based on the mesio-distal, bucco-lingual, apical deviations in distance and in angulation. The semi-robotic DN and static guide combination as a hybrid technique is an interesting method to improve the accuracy of flapless implant surgeries and can be used in cases where the anatomical landmarks are determinant factors for the implant placement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".