Is Traditional Chinese Medicine Use Associated with Worse Patient-reported Outcomes among Chinese American Rheumatology Patients?
Bibliographic record
Abstract
OBJECTIVE: Chinese Americans are a fast-growing immigrant group with more severe rheumatic disease manifestations than whites and often a strong cultural preference for traditional Chinese medicine (TCM). We aimed to examine TCM use patterns and association with patient-reported outcomes (PRO) among Chinese American rheumatology patients. METHODS: Chinese Americans actively treated for systemic rheumatic diseases were recruited from urban Chinatown rheumatology clinics. Data on sociodemographics, acculturation, clinical factors, and TCM use (11 modalities) were gathered. Self-reported health status was assessed using Patient Reported Outcomes Measurement Information System (PROMIS) short forms. TCM users and nonusers were compared. Factors independently associated with TCM use were identified using multivariable logistic regression. RESULTS: Among 230 participants, median age was 57 years (range 20-97), 65% were women, 71% had ≤ high school education, 70% were on Medicaid insurance, 47% lived in the United States for ≥ 20 years, and 22% spoke English fluently. Half used TCM in the past year; these participants had worse self-reported anxiety, depression, fatigue, and ability to participate in social roles and activities compared with nonusers. In multivariable analysis, TCM use was associated with belief in TCM, female sex, ≥ 20 years of US residency, reporting Western medicine as ineffective, and shorter rheumatic disease duration. CONCLUSION: Among these Chinese American rheumatology patients, TCM users had worse PRO in many physical and mental health domains. TCM use may be a proxy for unmet therapeutic needs. Asking about TCM use could help providers identify patients with suboptimal health-related quality of life who may benefit from targeted interventions.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".