Quantifying the visual appearance of sunscreens applied to the skin using indirect computer image colorimetry
Bibliographic record
Abstract
Summary Background There is no accepted method to objectively assess the visual appearance of sunscreens on the skin. Methods We present a method for sunscreen application, digital photography, and computer analysis to quantify the appearance of the skin after sunscreen application. Four sunscreen lotions were applied randomly at densities of 0.5, 1.0, 1.5, and 2.0 mg/cm2 to areas of the back of 29 subjects. Each application site had a matched contralateral control area. High‐resolution standardized photographs including a color card were taken after sunscreen application. After color balance correction, CIE L*a*b* color values were extracted from paired sites. Differences in skin appearance attributed to sunscreen were represented by ΔE, which in turn was calculated from the linear Euclidean distance within the L*a*b* color space between the paired sites. Results Sunscreen visibility as measured by median ΔE varied across different products and application densities and ranged between 1.2 and 12.1. The visibility of sunscreens varied according to product SPF, composition (organic vs inorganic), presence of tint, and baseline b* of skin (P < .05 for all). Conclusion Standardized sunscreen application followed by digital photography and indirect computer‐based colorimetry represents a potential method to objectively quantify visibility of sunscreen on the skin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".