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Record W4416925760 · doi:10.2196/75426

Patients’ Preferences Regarding Traditional Chinese Medicine for the Treatment of Chronic Obstructive Pulmonary Disease: Protocol for a Mixed Methods Study

2025· article· en· W4416925760 on OpenAlexvenueno aff
Shaonan Liu, Yan Yu, Xunxun Chen, Xinfeng Guo

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)Traditional Chinese medicineAlternative medicinePulmonary diseaseMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is now one of the top 3 causes of death worldwide. Traditional Chinese medicine (eg, herbal prescriptions and acupuncture), which has a long history of managing respiratory diseases, has shown positive effects in COPD management by alleviating dyspnea, improving lung function, and reducing the risk of acute exacerbations. Patients' values and preferences are undeniably important in medical decision-making and may affect treatment outcomes and patient adherence. OBJECTIVE: This study aims to investigate the preferences for traditional Chinese medicine of patients with COPD, the clinical outcomes they are concerned about, and the trade-offs involved in evaluating the factors that influence treatment selection, such as clinical effectiveness, cost, and adverse effects. METHODS: We will first update previous evidence through a systematic review of randomized controlled trials, examining the efficacy, safety, cost-effectiveness, and patient satisfaction of traditional Chinese medicines for COPD. Subsequently, an exploratory sequential mixed methods study will be conducted comprising qualitative and quantitative components. In this design, qualitative findings are collected first to inform and guide the subsequent quantitative data collection. Semistructured interviews will be conducted to explore in depth the preferences of patients with COPD for traditional Chinese medicine. Insights from these interviews will then be used to design questionnaires that quantitatively investigate the relative importance of different factors influencing patients' treatment decisions. Data integration will take place by connecting and interpreting the results from both the qualitative and quantitative steps, providing a comprehensive understanding of patient preferences. RESULTS: This study was approved by the ethics committee of Guangdong Provincial Hospital of Traditional Chinese Medicine on November 6, 2023 (ZM2023-405). A total of 18,188 articles published after 2016 were initially identified in English- and Chinese-language databases. The outline of the semistructured interview guide for this study has been developed. Further clinical evidence updates, qualitative interviews, and discrete choice experiments are still ongoing and will be completed by April 2026. CONCLUSIONS: This mixed methods study might provide important insights into the preferences of patients with COPD for traditional Chinese medicine, assessing trade-offs among efficacy, safety, cost, and other key factors that influence treatment decisions. This study is expected to deepen the understanding of patient-centered decision-making in the treatment of COPD. The findings are anticipated to guide clinical practice, inform policy development, and optimize the integration of traditional Chinese medicine with respiratory care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75426.

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 imitation

Not 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.

metaresearch head score (Codex)0.068
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.069
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.057
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0030.004
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0690.009

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.

Opus teacher head0.218
GPT teacher head0.558
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreProtocol

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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