Prevalence and risk factors of kidney stone disease in population aged 40–70 years old in Kharameh cohort study: a cross-sectional population-based study in southern Iran
Bibliographic record
Abstract
BACKGROUND: Kidney stone is the major cause of morbidity, and its prevalence is increasing in the world. This study aimed to assess the prevalence and risk factors of kidney stone in the adult population of southern Iran based on the data of the Kharameh Cohort Study. METHODS: This cross-sectional study was conducted on 10,663 individuals aged 40-70 years old, using the baseline data of Kharamah cohort study, which started in 2014. Among all participants, 2251 individuals had a history of kidney stone. The participants' demographic characteristics, behavioral habits, and the history of underlying diseases were investigated. The crude and Age Standardized Prevalence Rate of kidney stones was calculated. Also, logistic regression was used to identify the predictors of kidney stone. To check the goodness of fit index of the model, we used the Hosmer-Lemeshow test. All analyses were performed in STATA software. RESULTS: The prevalence of kidney stone was estimated 21.11%. Also, the Age Standardized Prevalence Rate in men and women was calculated 24.3% and 18.7%, respectively. The mean age of the participants was 52.15 years. Higher prevalence of kidney stone was seen in women aged 40-50 years (40.47%, p = 0.0001) and moderate level of social economic status (31.47%, p = 0.03), men with overweight (44.69%, p < 0.0001) and those in a very high level of social economic status (35.75%, p = 0.001). The results of multiple logistic regression showed that the chance of having kidney stone was 1.17 times higher in diabetic individuals, 1.43 times higher in hypertensive individuals, 2.21 times higher in individuals with fatty liver, and 1.35 times higher in individuals with overweight. The level of socio economic status, male sex, and age were the other factors related to kidney stone. CONCLUSION: In this study, underlying diseases such as fatty liver, diabetes, and hypertension as well as age, male sex, overweight, and high social economic status were identified as important risk factors for kidney stone. Therefore, identifying individuals at risk of kidney stone and providing the necessary training can greatly help to reduce this disease. However, health policymakers should prepare preventive strategies to reduce the occurrence of kidney stone.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".