Associations between HT, BMI, and allergic rhinitis in perimenopausal women
Bibliographic record
Abstract
BACKGROUND: Increasing evidence suggests that hormone therapy (HT) and obesity exert an influence on allergic rhinitis (AR). It is important to investigate the association and interactions between HT, BMI, and AR in perimenopausal women. METHODS: From May 2020 to March 2021, a cross-sectional survey was completed by patients who visited the Allergy Department and Gynecology Department of Shijitan Hospital. The patients completed a questionnaire and stratified analyses by BMI in tertiles were performed. Logistic analyses were performed to evaluate the relationships between HT, BMI, and AR. RESULTS: A total of 950 patients completed the study, among which, 393 patients were receiving HT. HT was found to be associated with increased risks for AR (OR = 1.51 [95% CI: 1.151-1.985]), asthma (OR = 3.61 [95% CI: 2.21-5.89]), and their accompanying symptoms (OR = 3.54 [95% CI: 2.146-5.831]). In lean women, the use of HT was significantly associated with a higher risks for AR (OR = 2.26 [95% CI: 1.31-3.91]), the time course of AR (OR = 2.54 [95% CI: 1.37-4.74]), hay fever (OR = 2.54 [95% CI: 1.37-4.74]), and accompanying symptoms (including canker sores, diarrhea, and stomachache) (OR = 2.26 [95% CI: 1.309-3.907]) when compared to normal or heavier weight women (course of AR: pinteraction = 0.032; hay fever; pinteraction = 0.006; accompanying symptoms: pinteraction = 0.009). CONCLUSIONS: HT can reduce the risk for AR in perimenopausal women. Lean women who used HT were at a higher risk for AR when compared to overweight women who used AR. There exists an interaction between HT and BMI that influences AR. Furthermore, HT and obesity increase the risk for AR by some common pathways, more follow-up work is needed to explore common pathways.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".