A comparative cross-sectional study on the quality of life in Grave’s disease patients: urban vs. rural perspectives
Bibliographic record
Abstract
Grave's disease affects numerous patients globally, but its impact on health-related quality of life (HR-QoL) in relation to geographical disparities remains under-explored. This cross-sectional study aimed to assess the influence of urban versus rural residence on HR-QoL among patients diagnosed with Graves' Disease in Rajasthan, India. One hundred seven Graves' disease patients from rural and urban endocrine centers were analyzed. The rural group included 52 patients (24 males, 28 females), averaging 38.9 ± 10.9 years of age, while the urban group had 55 (13 males, 42 females) with an average age of 39.1 ± 14.2 years. We found differences between rural and urban patients in terms of gender ratio, BMI, smoking habits, and obesity. Multivariable linear regression was used in both groups to determine the association between the baseline characteristics of Graves' patients from both areas and HR-QOL. Health-related quality of life, assessed via the SF-36 questionnaire, indicated higher general health and role emotional scores among urban patients. Our study found that the duration of Graves' disease in rural centers negatively impacted physical health scores. In urban patients, age and BMI influenced physical health, while gender and disease duration affected mental health scores in rural patients. Age impacted mental health in urban patients. Rural patients had a poorer quality of life compared to urban patients. Differences in gender distribution, BMI, smoking habits, and obesity rates revealed disparities in Graves' disease between rural and urban patients in India, highlighting the need for better healthcare infrastructure and awareness in rural areas.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".