Computerized Color Formulation for African‐Canadian People Requiring Facial Prostheses: A Pilot Study
Bibliographic record
Abstract
PURPOSE: The aim of this study was to investigate the effectiveness of spectrophotometry and a computerized color formulation system to predict pigment formulas for color mixing silicone elastomer to match the skin color of African-Canadian people. MATERIALS AND METHODS: In a prospective study, reflectance spectrophotometery was used to measure the skin color of 19 African-Canadian subjects. The spectral data for each subject was used in a computerized color formulation system to predict colorants required to mix silicone elastomer to match each subject's skin color. Delta-E values were recorded for each silicone sample in comparison to the subject's skin measurement. An analysis of variance was used to determine significance among variables, and a Tukey HSD post hoc test was used to assess paired comparisons. RESULTS: Delta-E decreased with iterative mixes of colored silicone for each subject, and pigment loading increased with iterative mixes. Delta-E values for the third iterative mix (fourth and final sample) ranged between 1.49 and 8.82. CONCLUSION: Spectrophotometry and computerized color formulation provide a foundation in the color matching procedure for facial prostheses that offers objectivity to an otherwise subjective task. Through further study of spectrophotometry and computerized color formulation, and with the development of pigment databases appropriate for the African-Canadian population, it may be possible to establish a precise and repeatable color matching system that predicts required colorants and controls metamerism.
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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 | 0.002 | 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) | 0.002 | 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".