Evaluating the reliability and repeatability of the digital color analysis system for dentistry
Bibliographic record
Abstract
This study was done to evaluate the reliability of the digital color analysis system (ShadeScan, CYNOVAD, Montreal. Canada) for dentistry. Sixteen tooth models were made by injecting the A2 shade chemical cured resin for temporary crown into the impression acquired from 16 adults. Surfaces of the model teeth were polished with resin polishing cloth. The window of the ShadeScan handpiece was placed on the labial surface of tooth and tooth images were captured, and each tooth shade was analyzed with the ShadeScan software. Captured images were selected in groups, and compared one another. Two models were selected to evaluate repeatability of ShadeScan, and shade analysis was performed 10 times for each tooth. And, to ascertain the color difference of same shade code analyzed by ShadeScan, CIE values of shade guide of Gradia Direct (GC, Tokyo, Japan) were measured on the white and black background using the Spectrolino (GretagMacbeth, USA), and Shade map of each shade guide was captured using the ShadeScan. There were no teeth that were analyzed as A2 shade and unique shade. And shade mapping analyses of the same tooth revealed similar shade and distribution except incisal third. Color difference () among the Shade map which analyzed as same shade by ShadeScan were above 3. Within the limits of this study, digital color analysis instrument for dentistry has relatively high repeatability, but has controversial in 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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".