Association of antibiotics use in preschool age with atopic and allergic skin diseases in young adulthood: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Overuse and misuse of antibiotics is a public health problem in low-income and middle-income countries. Although the association of antibiotics with atopic and allergic diseases has been established, most studies focused on prenatal exposure and the occurrence of disease in infants or young children. OBJECTIVE: To investigate the association of preschool use of antibiotics with atopic and allergic skin diseases in young adulthood. DESIGN: Population-based retrospective cohort. SETTING AND PARTICIPANTS: The first-year college students (n=20 123) from five universities were investigated. The sampled universities are located in Changsha, Wuhan, Xiamen, Urumqi and Hohhot, respectively. METHODS: We conducted a dermatological field examination and a questionnaire survey inquiring the participants about the frequency of upper respiratory tract infection (URTI) and the preschool antibiotics use (prior to 7 years old). The two-level probit model was used to estimate the associations, and adjusted risk ratio (aRR) and 95% CI were presented as the effect size. RESULTS: A total of 20 123 participants with complete information was included in the final analysis. The frequent antibiotics use intravenously (aRR 1.36, 95% CI 1.14 to 1.62) and orally (aRR 1.18, 95% CI 1.01 to 1.38) prior to 7 years old was significantly associated with atopic dermatitis in young adulthood. Similar trends could be observed in allergic skin diseases among those who use antibiotics orally and intravenously, with RRs of 1.16 (95% CI 1.01 to 1.34) and 1.33 (95% CI 1.13 to 1.57), respectively. CONCLUSIONS: Preschool URTI and antibiotics use significantly increases the risk of atopic and allergic skin diseases in young adulthood.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".