Accuracy of digital and conventional implant‐level impression techniques for maxillary full‐arch screw‐retained prosthesis: A crossover randomized trial
Bibliographic record
Abstract
OBJECTIVES: This study aimed to compare the accuracy of implant-level conventional and digital impressions for atrophied maxillary ridges. MATERIALS AND METHODS: Twelve participants with atrophied edentulous maxillary ridges received six implants. Six months later and after soft tissue maturation around healing abutments, a control cast was constructed using the final passive restoration for each patient. Two types of implant-level impression techniques were carried out for each patient: (1) conventional (splinted open-tray) impression technique and (2) digital impression technique. For both techniques, scan bodies were labeled from the most distal implant on the left side (A, B, C, D, E, and F) and scanning was made. Accuracy of both techniques was measured using in vitro (two-dimensional and three-dimensional) and in vivo (clinical) methods. Two-dimensional methods include measurement of the difference in linear distances AB, AC AD, AE, and AF. Geomagic software was used to assess the three-dimensional deviation between the two impression techniques using the superimposition of standard tessellation language files. The incidence and percentage of nonpassive frameworks and framework misfits of final restorations for both types of impression techniques were assessed using the single screw test. RESULTS: For all distances, digital impressions recorded significantly higher deviation from control measurements than conventional impressions. The highest two-dimensional linear deviation was noted for AF distance and the lowest difference was noted for AB distance. For all scan bodies, digital impressions recorded significantly higher three-dimensional deviation than conventional impressions. The highest three-dimensional deviation was noted with scan bodies C and D. Digital impressions recorded a significantly higher incidence of nonpassive frameworks and framework misfits than conventional impressions. [Correction added on 11 June 2024, after first online publication: In the preceding sentence, "digital impressions" was changed to "conventional impressions" in this version.] CONCLUSION: Within the limitations of this study, it could be concluded that the conventional implant-level impression technique showed greater in vitro and in vivo accuracy than the digital impression technique when used for full-arch maxillary fixed restorations on inclined implants.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".