Effect of techniques, trays and materials on accuracy of impressions clinically made
Bibliographic record
Abstract
Abstract Introduction Making accurate and dimensionally stable impressions to duplicate oral conditions and tooth morphology is an essential step of prosthetic dentistry for fabricating well-fitted indirect restorations and, consequently, ensure the longevity and success of the treatment. Several authors describe pros and cons of different impression techniques, although there is no unanimity among them about the best one. Objective This study evaluated casts’ accuracy made by different impression techniques, trays and materials. Material and method 10 patients were selected and 20 impressions from teeth #13 to #16 were performed using single-step (SS) and two-step techniques, made with metal stock and customized acrylic resin partial trays, vinyl polysiloxane and condensation silicone rubbers. Type IV gypsum was used to pour the casts. Three photographs of each patient (baseline), as their respective gypsum casts, were taken, measured in their interested buccal surface area. Comparisons of area values among experimental groups and baseline were performed. Data showed adherence to normal curve, being submitted to 3-way ANOVA/Bonferroni test (α=.05). Result Technique produced significant differences (P=0.02). SS technique was more accurate than BUR one (P=0.003; 95=1.22 to 5.98), but both resulted in similar casts to PVC, MOV and NR techniques, which were similar to each other (P>0.05). Conclusion SS technique showed the closest absolute values to baseline.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".