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Record W4238499342 · doi:10.21203/rs.3.rs-142168/v1

Self-Reported Weather Sensitivity is Associated with Clinical Symptoms and Structural Abnormalities in Patients with Knee Osteoarthritis: A Cross-Sectional Study

2021· preprint· en· W4238499342 on OpenAlexaboutno aff
Xue Yan, Yan Chen, Ding Jiang, Lin Wang, Xuezong Wang, Ming Li, Yuyun Wu, Min Zhang, Jian Pang, Hongsheng Zhan, Yuxin Zheng, Dao-Fang Ding, Yuelong Cao

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
FundersJiangxi University of Traditional Chinese MedicineShanghai University of Traditional Chinese MedicineNational Natural Science Foundation of China
KeywordsCross-sectional studyOsteoarthritisMedicineSensitivity (control systems)Physical therapyInternal medicinePathologyAlternative medicine

Abstract

fetched live from OpenAlex

Abstract Background Patients with knee osteoarthritis (KOA) often complain about clinical symptoms affected by weather-related factors. The purpose of the present study was to use cross-sectional analysis to determine whether weather sensitivity was associated with clinical symptoms, as well as structure abnormalities, in KOA patients. Methods Data from 80 participants were obtained from the Feng Hans Shi Effects on OA (FHS) study, an OA cohort study initiated in China in 2015. The weather sensitivity of each participant was determined by a self-reported questionnaire. The following measurements were used to assess clinical and biological outcomes: a visual analog scale (VAS) for pain; Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC); and blood levels of tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), and interleukin 1(IL-1). Structural changes identified via magnetic resonance imaging (MRI) were also evaluated. Independent sample t-tests, chi-square tests, Fisher’s exact tests, Cochran-Armitage tests for linear trends, and binary linear regression were used to evaluate the clinical characteristics, biomarkers, WOMAC, and Whole-Organ Magnetic Resonance Imaging Score (WORMS) of weather-sensitive KOA patients and non-weather-sensitive KOA patients. Results Most of the KOA participants (57.5%) perceived the weather as affecting their knee-joint clinical symptoms. Through logistic regression analysis, the presence of weather sensitivity was found to increase the risk of KOA participants reporting higher levels of WOMAC pain scores [OR = 3.3 (95% CI: 1.1, 9.9), P > 0.032], functional scores [OR = 5.5 (95% CI: 1.8,16.8), P > 0.003], total scores [OR = 3.3 (95% CI: 1.1, 10.2), P = 0.034], WORMS cartilage scores [OR = 3.1 (95% CI: 1.1, 8.5), P < 0.027], and marrow abnormality scores [OR = 3.0 (95% CI: 1.1, 8.1), P > 0.029]. Conclusions Weather-sensitive KOA patients were prone to show more serious clinical symptoms and structural abnormalities in their knee joints. Therefore, the existence of weather sensitivity may accelerate the progress of KOA.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.000

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 head0.095
GPT teacher head0.497
Teacher spread0.402 · 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 teacher head, not a consensus.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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