Trueness and precision of different intraoral scanners for shade assessment under variable light conditions-a cross-sectional study
Bibliographic record
Abstract
ABSTRACT Aim To compare the trueness and precision of shade assessment using different intraoral scanners under variable light conditions. Materials and Method To evaluate shade trueness and precision, color measurements (L*, a*, b*) were obtained using visual assessment, a spectrophotometer (SPM) (VITA Easyshade Advanced V), and two intraoral scanners, intra oral scanner-A(IOS-A) (Carestream 3700, Dexis IS ScanFlow) and intra oral scanner-B(IOS-B) (3Shape Trios 3), were used to scan 20 subjects by single assessor under natural daylight and operatory light. The color difference (ΔE) was calculated. Trueness and precision were assessed for each method across the two lighting conditions. Trueness of the test groups was compared by Analysis of Variance (ANOVA) followed by Tukey’s Post hoc Test for pairwise comparisons and precision by Kruskal Wallis test while pairwise comparison was done by Mann Whitney U Test. P values <0.05 was considered as statistically significant. Results For trueness, significant differences were observed among the visual method, IOS-A, and IOS-B. Tukey’s test revealed a significant difference between the trueness of IOS-A and IOS-B, while no significant differences were detected between the visual method and either scanner. Regarding precision, significant variations were found among the test groups. A significant difference was noted between the visual method and IOS-A, with a highly significant difference between IOS-A and IOS-B. However, no significant differences were observed between SPM and the visual method or IOS-B, nor between the visual method and IOS-B. Conclusions IOS-B shows better precision for shade determination than IOS-A, while the visual method is more reproducible than IOS-A. No significant differences in trueness and precision were found between the visual method and IOS-B.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".