Effects of Air Pollution on Disease Activity and Health-Related Quality ofLife of Systemic Lupus Erythematous Patients: An Iranian ObservationalLongitudinal Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Air pollution is one of the environmental factors that influences the pathogenesis of systemic autoimmune diseases, followed by the development and spread of inflammation and increased oxidative damage. Only a few studies have been conducted on the impact of air pollution on disease activity in patients with lupus, which mostly have focused on PM2.5 particles. Materials and Methods: We longitudinally studied 50 patients with lupus bimonthly in a 6-month period in Mashhad; one of the polluted cities of Iran. Disease activity and quality of life were examined according to SLEDAI2K, SLEQOL, and VAS criteria. The outdoor air pollutant was measured by monitoring the average concentration of nitrogen dioxide (NO2), carbon monoxide (CO), some particles less than 10 and 2.5 micrometers in diameter (PM <10, PM <2.5) and the level of temperature and humidity which were taken from the Meteorological Organization of Mashhad. Confounding factors such as medications were investigated by univariate and multivariate statistical analysis, specifically by GEE method. Results: The possible relation among various factors to SLEDAI, SLEQOL and VAS by two different univariate and multivariate analysis were studied. Our analysis indicated that spring season, decreased temperature, increased air pollutants including (PM2.5, and NO2) and increased humidity increase SLEDAI2K. Furthermore, the percent of polluted days directly correlates with Anti-dsDNA and NO2 significantly increases SLEQOL. Conclusion: Based on our findings, air pollution (particularly NO2 and PM2.5) has affected at least some aspects of the disease and the health-related quality of life (HRQL) of lupus patients. Further research is needed to confirm these findings.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it