Self-Reported Weather Sensitivity is Associated with Clinical Symptoms and Structural Abnormalities in Patients with Knee Osteoarthritis: A Cross-Sectional Study
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".