Influence of digital implant analog design on the positional trueness of an analog in additively manufactured models: An in‐vitro study
Bibliographic record
Abstract
BACKGROUND: Limited evidence exists regarding the accuracy of implant analog position in printed models, particularly when implant analogs with varying designs are used. PURPOSE: To evaluate the effect of digital implant analog (DIA) design on the trueness of their position in additively manufactured digital implant models (DIMs) and to compare with that of a conventional implant analog in a stone cast. MATERIALS AND METHODS: A dentate maxillary model with a conventional implant analog (Nobel Biocare Implant Replica 4.3 mm CC RP) at left second premolar site was digitized by using a laboratory scanner (3Shape D2000) and a (SB) scan body to generate the master standard tessellation language (STL) file (M0). 12 custom trays were fabricated on M0 file and conventional polyvinylsiloxane impressions of the model were made. All impressions were poured after inserting conventional implant analogs (Nobel RP Implant Replica) (Group A). Model was then digitized with an intraoral scanner (TRIOS 3) and the same SB, and DIMs with three different DIA designs (Nobel Biocare [Group B], Elos [Group C], and NT-trading [Group D]) were generated (Dental System-Model Builder). 12 DIMs of each design were additively manufactured and corresponding DIAs were inserted. All models were digitized by using the same laboratory scanner and SB, and these STLs were transferred to a 3D analysis software (Geomagic Control X), where the STL files of the models were superimposed over M0. Linear and 3D deviations at three selected points on SB (implant-abutment connection, most cervical point on SB, and most coronal point on SB) as well as angular deviations on two planes (buccolingual and mesiodistal) were calculated. Analysis of variance (ANOVA) and Bonferroni corrected t-tests were used to analyze the trueness of implant analog positions (α = 0.05). RESULTS: The interaction of main effects significantly affected linear (p < 0.001) and angular deviations (p = 0.020). At point 1, group D had higher deviations than groups A and B (p ≤ 0.015). In addition, groups A and D had higher deviations than group B at point 4 (p < 0.001). While group C had similar linear deviations to those of other groups at point 1 and point 4 (p ≥ 0.192), the differences among test groups at point 2 were nonsignificant (p ≥ 0.276). Group B had lower angular deviations than groups C (p = 0.039) and D (p = 0.006) on buccolingual plane. CONCLUSIONS: Analog design affected the trueness of analog position as proprietary, pressure/friction fit DIA (group B) had higher linear trueness than screw-retained DIA (Group D) and conventional implant analog (group A). In addition, pressure/friction fit DIA had the highest angular trueness among tested DIAs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".