Coloration of Silicone Prostheses: Technology versus Clinical Perception. Is There a Difference? Part 2, Clinical Evaluation of a Pilot Study
Bibliographic record
Abstract
PURPOSE: The aim of this investigation was to explore the relationship between an objective computer measurement of color difference (ΔE) and subjective clinical opinion of a "good" color match between silicone samples and skin. MATERIALS AND METHODS: In Part 1 of this study, silicone samples were colored to match the skin of 19 African-Canadian subjects based on spectrophotometric measurements and pigment formulae determined by computerized color formulation software. Four iterative samples were prepared for each subject; a ΔE value was recorded for each sample to represent the color difference between the silicone sample and skin. In this article, Part 2, five judges independently assessed the color match of the silicone samples to the skin of each of the subjects. Skin and silicone samples were rated on a five-point scale as a measure of "color match." A multivariate analysis was used to determine relationships between judges' assessments and the following variables: color difference between silicone and skin (ΔE), pigment loading, and skin characteristics (L*, a*, b*). RESULTS: There was a positive correlation between judges' scores and low ΔE values for the first two samples. All judges rated the first sample a poorer color match than the fourth sample (p < 0.015). The third sample performed better overall according to judges. Increased pigment loading in the fourth sample resulted in poorer scores. A trend was observed in pigment selection based on skin values, though no significant relationships were determined. CONCLUSION: Spectrophotometry and computerized color formulation technology offer an enhanced understanding of color for its artistic application in facial prosthetic treatment. While some correlation between the objective and subjective assessments of color match exist, it is not a simple relationship. Further study is required to better understand the relationship between technology and clinical perception, specifically in objective and subjective assessments of a "good" color match of silicone to skin.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".