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Record W4322710923 · doi:10.2196/44541

Health Care–Seeking Behaviors, Disease Progression, Medications, Knowledge of, and Attitudes Toward Systemic Lupus Erythematosus in China: Cross-sectional Survey Study

2023· article· en· W4322710923 on OpenAlexvenueno aff
Zonglin Dai, Xinxiang Huang, Fei Yuan, Li T, Baozhao Xie, Lin He, Pingting Yang, Xueyi Li, Shuiming Xu, Jinjun Zhao, Yukai Wang, Xiang Peng, Simin Wei, Wei Huang, Jingyang Li, Jing Liang, Xiu-Hua Liu, Zhiming Zhang, Renpeng Zhang, Eric H. Y. Lau, Zhiming Lin

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
FundersSun Yat-sen University
KeywordsMedicineDiseaseSystemic lupus erythematosusLupus nephritisCross-sectional studyLogistic regressionHealth careInternal medicineImmunologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Systemic lupus erythematosus (SLE) is a systemic autoimmune disease involving multiple organs throughout the body. The health care-seeking behaviors, disease progression of SLE, and patients' knowledge of and attitudes toward SLE have not been characterized in China. OBJECTIVE: The aim of this study was to depict the health care-seeking behaviors, disease progression, and medications in patients with SLE and to examine the factors associated with their disease flares, knowledge, and attitudes toward SLE in China. METHODS: We conducted a cross-sectional survey in 27 provinces in China. Descriptive statistical methods were used to depict the demographic characteristics, health care-seeking behaviors, medications, and health status. Multivariable logistic regression models were used to identify the factors associated with disease flares, medication changes, and attitudes toward SLE. An ordinal regression model was used to examine the factors associated with the knowledge of the treatment guidelines. RESULTS: We recruited 1509 patients with SLE, and 715 had lupus nephritis (LN). Approximately 39.96% (603/1509) of the patients with SLE were primarily diagnosed with LN, and 12.4% (112/906) developed LN (mean time 5.2 years) from non-LN. Patients whose registered permanent residences or workplaces in other cities from the same province and adjacent provinces seeking health care accounted for 66.9% (569/850) and 48.8% (479/981) of the patients with SLE in the provincial capital cities, respectively. Mycophenolate mofetil was the most commonly used immunosuppressive drug in patients without LN (185/794, 23.3%) and patients with LN (307/715, 42.9%). Femoral head necrosis (71/228, 31.1%) and hypertension (99/229, 43.2%) were the most common adverse event (AE) and chronic disease during treatment, respectively. Change of hospitals for medical consultation (odds ratio [OR] 1.90, 95% CI 1.24-2.90) and development of 1 chronic disease (OR 3.60, 95% CI 2.04-6.24) and AE (OR 2.06, 95% CI 1.46-2.92) and more were associated with disease flares. A pregnancy plan (OR 1.58, 95% CI 1.18-2.13) was associated with changes in medication. Only 242 (16.03%) patients with SLE were familiar with the treatment guidelines, and patients with LN tended to be more familiar with the disease (OR 2.20, 95% CI 1.81-2.68). After receiving treatment, 891 (59.04%) patients changed their attitudes toward SLE from fear to acceptance, and patients with college education or higher (OR 2.09, 95% CI 1.10-4.04) were associated with a positive attitude toward SLE. CONCLUSIONS: A large proportion of patients seeking health care in the provincial capital cities of China migrated from other cities. Persistent monitoring of potential AEs and chronic diseases during SLE treatment and managing patients who changed hospitals for medical consultation are essential for controlling disease flares. Patients had insufficient knowledge about SLE treatment guidelines and would benefit from health education to maintain a positive attitude toward SLE.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.057
GPT teacher head0.422
Teacher spread0.365 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2023
Admission routes1
Has abstractyes

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