Effect of Patch Type on the Cumulative Irritation Potential of 4 Test Materials
Bibliographic record
Abstract
Background: Many different patch systems are available for predicting contact dermatitis. It is important to determine the ideal patch to meet the objective of the testing method. Objective: The 21-day cumulative irritation test is well accepted for predicting irritation after repeated exposures. The patch type must allow separation of materials to predict irritation potential in the marketplace. Three patch systems were compared to determine which best provides this separation and prediction. Methods: Four test materials were evaluated using 3 patch systems in a 21-day cumulative irritation test. Tested were water, 0.06% sodium lauryl sulfate (SLS), and 2 underarm products (UAP), one having lower and one having higher irritation potential. The patch types were; Webril pad and 8-mm and 12-mm Finn Chambers. Results: Both the 12-mm Finn Chamber and Webril pad showed the ability to differentiate the higher irritating UAP and the 0.06% SLS from the lower irritation UAP product and water. The 8-mm Finn chamber was less discriminating, showing the 0.06% SLS to be the same as water and the lowerirritating UAP. Conclusion: The Webril pad and the 12-mm Finn Chamber are better at discriminating irritation potential than is the 8-mm Finn Chamber. The 12-mm Finn Chamber might also allow discrimination with a lower degree of irritation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".