Influence of the quantity of sunscreen applied on the ability to protect against ultraviolet‐induced polymorphous light eruption
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Despite the fact that most people apply less sunscreen than the 2 mg/cm(2) required to measure sun protection factor (SPF), there is a lack of clinical data on the protection afforded from lower applied quantities. The aim of this study was to compare the ability of sunscreens to protect against UV-induced polymorphous light eruption (PLE) when applied at 2 mg/cm(2) and 1 mg/cm(2) . METHODS: Two SPF 45 sunscreens (one with a high level and one with a low level of UVA protection) were applied at 2 mg/cm(2) and 1 mg/cm(2) to four randomized 6 × 6 cm areas on the upper thorax of 15 female patients with a typical history of PLE. The areas were exposed daily to increasing UVA-UVB radiation until a PLE reaction was detected or a maximum of five consecutive days. RESULTS: The proportion of patients who developed a PLE reaction with the high UVA-protection sunscreen was significantly lower (0%) than with the low UVA-protection sunscreen (73%) when both sunscreens were applied at 2 mg/cm(2) (P = 0.004). At 1 mg/cm(2) , 33% and 80% of patients presented a PLE reaction with the high and low UVA-protection sunscreen, respectively (P = 0.064). CONCLUSION: A high SPF and high UVA-protection broad spectrum sunscreen was able to protect the majority of patients from the development of UV-induced PLE reaction even at 1 mg/cm(2) .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".