Effects of temperature on daily hospital visits for urticaria in Nanchang, China: a distributed lag nonlinear time series analysis
Bibliographic record
Abstract
OBJECTIVE: This study evaluated temperature's lagged effects on urticaria outpatient visits in Nanchang, China (2017-2022), and identified sensitive populations through age/gender stratification. METHODS: Using a distributed lag nonlinear model (DLNM), we analyzed 71,779 urticaria visits, adjusting for humidity, weekday, holidays, and seasonal/long-term trends. Temperature effects (cold: 5th/25th percentiles; hot: 75th/95th percentiles) were compared to the 50th percentile. RESULTS: Temperature exhibited non-linear and delayed impacts. Daily averages >19.9°C initially increased then decreased urticaria risk, peaking at 29°C with a 15-day lag (RR=1.74, 95% CI:1.63-1.86). No adverse effects occurred below 19.9°C. Individuals aged ≥60 were most vulnerable: at 29°C with a 16-day lag, RR surged to 2.31 (95% CI:1.99-2.70). CONCLUSION: Hot increases urticaria outpatient visits, while cold reduces risk. These findings highlight temperature-specific prevention strategies, particularly for older adults.
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.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".