Characterization of chronic itch in college students in China
Bibliographic record
Abstract
Chronic itch affects a wide range of individuals and has a noticeable influence on human health. However, epidemiological characteristics of chronic itch have not yet been adequately addressed.To investigate the prevalence and associated factors for chronic itch.This was a cross-sectional study of first-year students in five universities in different regions of China. Chronic itch was defined as moderate-to-severe itch for more than six weeks. Social and behavioural factors were measured through an online questionnaire survey. Environmental factors including humidity, temperature and levels of air pollutants at district/county level were obtained from public datasets. Generalized linear-mixed models were used to fit multi-level data.A total of 27,144 students were enrolled to the universities and 18,360 subjects who completed the survey were included in the final analysis. The mean age of participants was 18.3 ± 0.8 years, and 51.1% were women. The point prevalence of chronic itch was 16.9%. Chronic itch was associated with female sex (OR = 0.78, p < 0.001) and was inversely associated with higher parental educational levels. With respect to environmental factors, chronic itch was significantly associated with relative humidity (OR = 1.01, p = 0.02), temperature (OR = 1.03, p<0.001) and O3 (OR = 0.91, p < 0.001). Regarding behavioural factors, chronic itch was significantly associated with smoking, exposure to second-hand smoke, drinking alcohol, less physical activity, intake of pepper, preference for spicy food, bathing (temperature, frequency, and duration) and sunscreen.Chronic itch affects a substantial proportion of Chinese college students, and is attributable to demographic, dermatological, environmental and behavioural factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".