Impact of diurnal temperature range on rhinitis in Lanzhou, China: Accounting for COVID-19 effects
Bibliographic record
Abstract
Rhinitis is a global public health issue. A distributed lag non-linear model (DLNM) was used to examine the impact of diurnal temperature range (DTR) on nonallergic rhinitis (NAR) and allergic rhinitis (AR) in Lanzhou, China. Stratified analyses by age, gender, seasons, and the COVID-19 period were conducted, alongside extreme-DTR and attributable risk analyses . Using 37,225 cases, we found: 1. The nonlinear DTR-rhinitis relationship: high- and low-DTR acutely impacted AR, while primarily high DTR moderately affected NAR during full and pre-COVID-19 periods. 2. COVID-19 transformed the adverse impact of low-DTR into protective effects. 3. In full study period, AR showed age heterogeneity, and NAR displayed age and gender differences . In pre-COVID-19 study period, AR had age and gender variations. The age < 18 group, vulnerable to NAR and AR, requires concerns. 4. Low-DTR exhibits protective effects during cold seasons, while it poses adverse effects and increased risks in warm seasons. 5. Moderate high DTR contributed most to rhinitis outpatients. In conclusion, NAR and AR demonstrate distinct responses to short-term DTR exposure, which may be influenced by COVID-19 and seasonal factors. Future research is essential to assess the long-term impacts of DTR and COVID-19 effects on NAR and AR.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".