Determining accuracy of sleeveless tooth-supported surgical guide for guided implant surgery: A retrospective observational study
Bibliographic record
Abstract
Aim: The advances in CAD/CAM technique have enabled digital data from the virtual setting to be transferred to the real clinical output using surgical guides. Evidence for the accuracy of surgical guides with metal sleeves has been documented. However, the sleeveless surgical guide has not yet been analyzed for its accuracy in clinical situations. The purpose of the study is to determine the accuracy of the sleeveless tooth-supported surgical guide for guided implant surgery. Materials and Methods: This retrospective observational study evaluates data of 25 patients selected randomly with single implants placed using the sleeveless surgical guide, which was collected from a single center at Thane. Cone-beam computed tomography of the treatment planning and the post-implant placement data were superimposed on each other and analyzed using Evalunav (3.0 version, Claronav, Toronto, Canada) software. Descriptive analysis was done. Coronal, apical, and angular deviations were calculated and assessed using a one-sample t-test. Results: It was found that the mean coronal deviation between the planned and placed implants was 0.73± 0.4 mm (0.12–1.81 mm), the mean apical deviation was 0.81± 0.43 mm (0.12–1.66 mm), and the angular deviation was 2.32° ± 0.98° (0°–4.78°). There was a statistically highly significant difference between the achieved and accepted values (P < 0.01) with a lower deviation achieved when compared with the acceptable values. Conclusion: It was concluded that the 3d printed tooth-supported surgical guide with no metal sleeves has acceptable accuracy for placing dental implants using the guided technology.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".