Trueness and Passivity of Digital and Conventional Implant Impressions in Edentulous Jaws: A Prospective Clinical Study
Bibliographic record
Abstract
AIM: To compare the linear and angular deviations of conventional implant (CI) and digital implant (DI) impression techniques in edentulous jaws with four or six implants. MATERIALS AND METHODS: Twenty participants (12 men, 8 women; mean age 58.6 years) with complete edentulous maxillary (n = 8) or mandibular (n = 12) arches were included. Each patient received four or six dental implants (Straumann BLX). Both CI and DI were performed using randomized sequences. Linear and angular deviations were measured between the reference scan (coordinated measuring machine) and the CI (desktop scanner) and DI (intraoral scanner, IOS) using CATIA software (Dassault Systèmes). Framework passivity was evaluated using the Sheffield one-screw test. The Shapiro-Wilk test determined data normality (p < 0.05), and nonparametric statistical tests were applied using statistical software. RESULTS: Descriptive statistics showed a mean linear discrepancy of 29.05 (84.80 μm) for CI and 6.95 (154.10 μm) for DI, with angular deviations of 0.06° (0.36°) for CI and 0.05° (1.40°) for DI. No statistically significant differences were found in linear (p = 0.38) or angular (p = 0.12) measurements between CI and DI. Framework passivity testing showed that both techniques achieved passive fit in 17 out of 20 cases (85%), with the reference scan achieving passivity in 18 (90%) cases. Distal implants, particularly in the upper jaw, exhibited greater discrepancies, but none were statistically significant. CONCLUSIONS: No significant differences in trueness were found between CI and DI techniques. Both methods demonstrated comparable trueness and framework passivity, supporting the use of IOS as a reliable alternative to CI in edentulous jaws with multiple 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".