Computer‐aid dynamic implant placement surgery – A cases series and technique notes
Bibliographic record
Abstract
Background In recent years, there has been a growing interest in guided implant surgery. The results of several published indicate that static guided surgery is better accuracy than free hand surgery. Poor implant positioning increases the risk of biological complications and biomechanics overload. Guided implant surgery improved the implants placement in a proper position. However, there are several limitations of static guided surgery, such as stability, faulty of ISO caused inaccuracy of image. Aim/Hypothesis The aim of this present is to assess the computer-aid dynamic guided implant placement surgery accuracy and clinic efficiency. Material and Methods Total 9 partially edentulous patients (4 female, 5 male) were included and received 16 implants placed under computer-aid dynamic guided surgery, One patient had insufficient bone width and height, used navigation to determine the inferior of sinus lateral wall augmentation, performed antrostomy according to the frame design by Navident software. (Navident, ClaroNav, Canada). After cone-beam CT acquisition, DICOM files were imported and merge with predetermined crown STL data, planning restorative driven implant placement. 11 implants placed in healed ridges, 5 implants in immediate placement. Post-operation assessment use the EvaluNav application to evaluate the deviations between the planned and the actual position of the implants. Estimate the deviations of entrance point, apical point (3d), apical point (v) and angle deviations. Results The EvaluNav application estimate the mean deviation of entrance point is 0.93 mm, the apical deviation (3d) is 1.61 mm, the apical deviation(v) is 0.90 mm and the angle deviation is 3.43 degree. The application navigation is easily to determine the inferior of the sinus walls, more efficiency for antrostomy and avoid the complications of sinus elevation procedure. Conclusion and Clinical Implications Based on the limitations of this presentation, it can be concluded that the accuracy of computer-aid dynamic guided implant surgery is within clinical acceptance, decrease human errors. The safety range of at least 2 mm is need respected. Navigation guided of antrostomy for lateral sinus elevation augmentation is clinic efficiency and save time.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".