Marginal and internal fit of CAD/CAM frameworks in multiple implant‐supported restorations: Scanning and milling error analysis
Bibliographic record
Abstract
BACKGROUND: Despite computer-aided design and computer-aided manufacturing (CAD/CAM) technology improving prosthesis fit, errors inherent to digital workflow still exist. PURPOSE: To measure scanning/milling errors, and identify factors influencing marginal (MD) and internal discrepancy (ID). MATERIALS AND METHODS: After scanning, 22 conical abutments in 5 master casts, 6 suprastructures with more than 2 implants (3, 4, and 6) were CAD designed. Angular deviation and errors in the vertical/horizontal planes were analyzed using a coordinate measuring machine (CMM). CAD suprastructures were milled and MD/ID evaluated with micro-computed tomography (CT) and optic microscopy (OM) at one screw test (OST) and final fit test (FFT). RESULTS: Mean scanning errors, at the vertical/horizontal planes, and angulation error were 3 μm ± 13, 44 μm ± 34, 0.3° ± 0.2°, respectively. Angulation errors nearly double in structures >3 abutments (0.26°vs 0.4°). OM MD in FFT/OST was 57.7 μm ± 13.9/100.7 μm ± 34.6, respectively. Micro-CT FFT-MD was 38.9 μm ± 12.8. Lineal/perimetral ID was 49.6 μm ± 11.9 and 108.2° ± 41.8, respectively. Structures >3-implants were 2.3 times more likely to present higher MD (CI95%:0.4-13.6). Nearly all the internal horizontal gap was due to scanning errors (44 of 49.6 μm). Horizontal scanning errors were three times more likely to present greater ID (CI95%:0.5-17.4). CONCLUSION: Horizontal plane scanning errors are greater than vertical errors. Scanning angulation/milling errors are higher for suprastructures>3implants. Scanning/milling errors are associated with ID/MD, respectively, leading to micro-gap formation. A CMM reduces scanning errors in >3-implant-frameworks before milling the final piece.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".