Précision du positionnement implantaire : chirurgie guidée dynamique VS chirurgie guidée statique et chirurgie à main levée
Bibliographic record
Abstract
Objective: the objective of this systematic review was to evaluate the accuracy of implant surgery using a dynamic navigation system and to compare it with other techniques. Methods: an electronic search was conducted on 4 databases until April 2022. Manual searches were also included. Out of 158 studies, 10 were selected. The quality of prospective studies and clinical case series was determined using the Newcastle Ottawa Quality Assessment Scale. The quality of randomized controlled trials was assessed using the Cochrane Risk of Bias Tool Rob2. Results: the 10 included studies allowed the evaluation of more than 1300 implants. Accuracy was assesed by comparing the position of the implant during virtual planning with the actual position of the implant at the end of surgery. This comparison was made possible by performing a pre- operative CBCT as well as a post-operative CBCT. Our systematic review confirmed that a greater accuracy was obtained when using surgical navigation in comparison with freehand surgery with a mean angular deviation of 3.68 degrees versus 8.07. However, for studies comparing static and dynamic guided surgery techniques no significant difference was found in terms of accuracy. Conclusion: until now, static guided surgery has been considered the first option in implant surgery because of the large amount of data available on its accuracy. It is indeed a reliable and well- documented method. Current studies do not show that dynamic navigation is superior in terms of accuracy. However, dynamic navigation offers a better intraoperative reactivity and could be used in various clinical situations. All these qualities make surgical navigation a very attractive solution suitable to any implant surgery.
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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 | 0.019 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".