In Vitro Implant Impression Accuracy Using a New Photopolymerizing <scp>SDR</scp> Splinting Material
Bibliographic record
Abstract
PURPOSE: The study aims to evaluate three-dimensionally (3D) the accuracy of implant impressions using a new resin splinting material, "Smart Dentin Replacement" (SDR). MATERIALS AND METHODS: A titanium model of an edentulous mandible with six implant analogues was used as a master model and its dimensions measured with a coordinate measuring machine. Before the total 60 impressions were taken (open tray, screw-retained abutments, vinyl polysiloxane), they were divided in four groups: A (test): copings pick-up splinted with dental floss and fotopolymerizing SDR; B (test): see A, additionally sectioned and splinted again with SDR; C (control): copings pick-up splinted with dental floss and autopolymerizing Duralay® (Reliance Dental Mfg. Co., Alsip, IL, USA) acrylic resin; and D (control): see C, additionally sectioned and splinted again with Duralay. The impressions were measured directly with an optomechanical coordinate measuring machine and analyzed with a computer-aided design (CAD) geometric modeling software. The Wilcoxon matched-pair signed-rank test was used to compare groups. RESULTS: While there was no difference (p = .430) between the mean 3D deviations of the test groups A (17.5 μm) and B (17.4 μm), they both showed statistically significant differences (p < .003) compared with both control groups (C 25.0 μm, D 19.1 μm). CONCLUSIONS: Conventional impression techniques for edentulous jaws with multiple implants are highly accurate using the new fotopolymerizing splinting material SDR. Sectioning and rejoining of the SDR splinting had no impact on the impression accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".