Adverse Cosmetic Reactions in a Pediatric Population Reported to the Chongqing Drug Administration in China from 2017 to 2021
Bibliographic record
Abstract
Abstract: Background: Despite their ubiquitous use and several safety incidents involving cosmetics for children in China, there is little research on adverse reactions to cosmetics in children. Objectives: We assessed the cosmetic adverse reactions (CARs) reports submitted to the Chongqing Drug Administration in China for children, to understand the characteristics of CARs in a pediatric population and determine whether useful insights can be derived. Methods: We extracted the data file of the Chongqing Drug Administration's cosmetic adverse events reporting system from 2017 to 2021, and screened the information of people under the age of 18 years for analysis. Results: A total of 589 children were reported; of them, 475 female children and 114 male children, aged 1–17 years, and 89.6% were diagnosed with cosmetic contact Dermatitis. Itching and burning were the most prominent symptoms and accounted for 83.4% and 40.2%, respectively. The most frequently reported clinical sign was erythema (73.3%) followed by papule (37.9%). The face is the most vulnerable location to lesions, accounting for 80.8% of all areas, with girls having a significantly higher rate of facial and scalp damage than boys. The majority of the CARs were reported with cream, lotion, and toner for the skin (45.9%) and facial or body cleansing products (15.4%), and most of these products were purchased from authoritative shops. Conclusion: Although adults are the main group of people who use cosmetics, due to the special physiological structure of children, the safety of children's cosmetics should be given more attention. In addition, pediatricians and dermatologists should be active in submitting reports of adverse cosmetic events and encouraging consumers to do so likewise in situations in which a product adversely affects a child's health.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".