Association between Disease‐Specific Quality of Life and Complementary Medicine Use in Patients with Rhinitis in Taiwan: A Cross‐Sectional Survey Study
Bibliographic record
Abstract
Rhinitis is a common medical condition and can seriously impact patients' quality of life. The objective of this study was to investigate the association between disease-specific quality of life and use of complementary and alternative medicine (CAM) modalities among Taiwanese rhinitis patients. A cross-sectional survey was undertaken at the outpatient department of otolaryngology in a medical center in Taiwan. Sociodemographic information, disease-specific quality of life (Chinese version of the 31-item Rhinosinusitis Outcome Measure, CRSOM-31), and previous use of CAM modalities for treatment of rhinitis of the patients were ascertained. Factor analysis was performed to reduce the number of CAM modalities. The resulting factors were analyzed for their association with CRSOM-31 score using linear regression analyses. Results from the multiple linear regression analyses indicated that Factor 1 (traditional Chinese medicine), Factor 2 (mind-body modalities), Factor 3 (manipulative-based modalities), female sex, and smoking were significantly associated with a worse disease-specific quality of life. In conclusion, various CAM modalities, female sex, and smoking were independent predictors of a worse disease-specific quality of life in Taiwanese patients with rhinitis.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".