Evaluation of the accuracy of digital workflow for implant‐supported full‐arch fixed dental prostheses using a novel micro‐CT measurement technique
Bibliographic record
Abstract
PURPOSE: This study aims to evaluate the accuracy of fit of full-arch implant titanium frameworks fabricated from a fully digital workflow using a novel micro-CT measurement technique. MATERIALS AND METHODS: A 3D-printed model with four implant analogs was fabricated. A baseline micro-CT was obtained after placing temporary cylinders on the model. Next, the printed model was scanned with an intraoral scanner (TRIOS 5), and the STL files were used to fabricate 10 titanium frameworks. Each framework was placed back on the model, and another micro-CT was taken under two conditions: single screw test (SST-CT) and final fit test (FFT-CT), and the measurements were compared to the baseline. Framework passivity was evaluated using a single-screw test (SST) and a screw-resistance test (SRT). The accuracy of the intraoral scans was assessed by superimposing the 10 scans with a laboratory scan STL to determine if the misfit was due to scanning or milling and designing errors. RESULTS: None of the frameworks was deemed acceptable using SST-CT, and only three had an acceptable fit using FFT-CT. SST and SRT non-passivity rates were 60% and 80%, respectively. Superimposition analysis revealed that only two intraoral scans used for framework fabrication fell within the acceptable deviation range of 150 microns, suggesting a high tendency for scanning errors and a possible milling or designing error in two samples. CONCLUSION: The results show a significant level of misfit. This suggests that the full-digital workflow for full-mouth rehabilitation can present some limitations. Due to the rapid advancement in intraoral scanning, further studies are required to validate these findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".