Comparison of reproducibility and workability of single and adjacent implant placement protocol under dynamic real time navigation systems between operators: A clinical trial
Bibliographic record
Abstract
Dynamic navigation (DN), a computer-assisted technique integrating CBCT data and real-time video, has emerged as a promising approach to place implants in the recent years. This study aims to evaluate the consistency and ease of use of a dynamic navigation system for implant placement by comparing the accuracy in single and adjacent implant placements and workability achieved by three different operators. This study included Forty-eight patients requiring dental implants, total of sixty implants were randomly assigned to 3 operators of varying experiences, the implants were planned and placed under DN. The accuracy of implant placement were measured in terms of mesio-distal, apico-coronal displacement and angulations using Evalunav application ( Navident, Claronav, Canada ). Secondary outcome variables are the number of errors encountered during the procedure and the time taken for the procedure by different practitioners. Kruskal Wallis Test followed by the Post hoc Mann Whitney U test. The level of significance was set at P < 0.05. There were no significant differences in the accuracy of single implants (P > 0.05). For adjacent implants (T1), the displacement in mesiodistal direction was significantly different (P = 0.003) and also for apico-coronal position of T1-abutment group when compared to controls with a P value of 0.026. Experienced surgeons had the highest error rates as well and longest time (18.27 ± 5.62 versus 15 min). The operating surgeon do not determine the accuracy rather the navigation system comes with a steep learning curve that needs to be acquired prior to practicing the same.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".