Influence of implant macrodesign and insertion connection technology on the accuracy of static computer‐assisted implant surgery
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate the effect of three different macrodesigns and two different insertion devices on the accuracy of static computer-assisted implant surgery (sCAIS). MATERIALS AND METHODS: Ninety implant replicas with three different macrodesigns: Soft tissue level (TL), bone level (BL), and bone level tapered (BLT) were placed in 30 dental models with two implant insertion devices: Guided adapter and guided screwed-in mount. Preoperative and postoperative positions of implants were compared and the mean angular deviation, crestal, and apical three-dimensional (3D) deviation were calculated for each implant macrodesign and each insertion device. Data were analyzed using analysis of variance, post hoc t-tests and Bonferroni-Holm's adjustment method. P values less than .05 were considered statistically significant. RESULTS: BLT implants had lower mean 3D deviation values at the crest and the apex when compared with 3D deviations with BL and TL implants (P < .05). Also, BLT implants had lower angular deviations, when compared with BL and TL Implants, however, angular deviations were not statistically significant (P > .05). Considering the insertion device method, no significant differences were noted between insertion devices irrespective of the deviation analyzed. CONCLUSION: The macrodesign of dental implants may have an influence on the accuracy of sCAIS, with tapered designs offering slightly better positional accuracy than parallel-walled macrodesigns independent on the method of insertion used.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".