The influence of guided sleeve height, drilling distance, and drilling key length on the accuracy of static Computer‐Assisted Implant Surgery
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the effect of guided sleeve height, drilling distance, and guided key height on accuracy of static Computer-Assisted Implant Surgery (sCAIS). MATERIALS AND METHODS: Pre and post-operative positions of implants placed in duplicate dental models were compared and recorded after placement of implants according to a standardized treatment planning and execution sCAIS protocol. Guided sleeve heights: 2 mm, 4 mm, 6 mm and guided key heights: 1 mm and 3 mm were equally randomized in six test groups with varying implant lengths (10-16 mm) and surgical drilling protocols. The mean crestal and apical three-dimensional (3D) deviation, as well as the angular deviation were calculated for each group. Data was analyzed using multivariate analysis anova. P values less than .05 were considered statistically significant. All P values of post-hoc tests were corrected for multiple testing using Bonferroni-Holm's adjustment method. RESULTS: 3D implant positioning accuracy was not significantly affected by the difference in sleeve height alone or by the implant length alone (P > .05). However, 3D and angular deviation values became significantly higher as the total drilling distance below the guided sleeve increased and significantly became lower as the guided key height above the sleeve increased. 18 mm drilling distance resulted in a significantly higher deviation, when compared to 14 mm or 16 mm drilling distances, irrespective of sleeve height or implant length (P < .01). 3 mm key height resulted in significantly less 3D deviation than 1 mm key height (P < .01). CONCLUSION: Decreasing the drilling distance below the guided sleeve, by using shorter sleeve heights or shorter implants can significantly increase the accuracy of sCAIS.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".