Use of Consumer Product Ingredients for Patch Testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background:Patch testing for suspected sensitivity to cosmetics and other personal care products is usually done by testing with nonirritating products “as is” and by panels of antigens likely to contain causative ingredients. Most allergic reactions are reportedly due to sensitivity to either fragrances or preservatives. Although most preservatives found in patients' products are available for patch testing, only a small number of fragrance ingredients are available, and fragrance components are seldom labeled. Most personal care products contain many other ingredients, and unless the patient reacts to the whole product and the ingredients are obtained from the manufacturer, most of these are seldom tested. Methods:Investigators reviewed patch-test records of patients who presented with eruptions compatible with the use of their personal care products and who were tested with available ingredients that were listed on the labels of products they were using. This allowed testing with many ingredients in products that are too irritant for “as is” testing. Some of the results included those of persons who were tested in other series, so these were separated. Results:Of patch tests with 52 cosmetic ingredients also tested in other series, 3.4% produced at least one + or greater reaction. Of those antigens tested only when present in products used by the patient, 55 of the 121 ingredients produced at least one + reaction, and about 3.6% of the test results were positive. Conclusions:Adding ingredients found in the patient's personal care products to patch tests done on those compatible with exposure increases the positive yield in patch testing, and the number of positive results is likely to increase as more ingredients are available for testing.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 it