Assessment of Bone Mineral Density in Patients Undergoing Hemodialysis; An Iranian Population-Based Study
Bibliographic record
Abstract
Background: End-stage renal disease (ESRD) is a condition in which bone turnover and metabolism is impaired; thus, osteoporosis and low bone density are subsequently inevitable. We aimed to determine bone mineral density (BMD) and biochemical markers, and associated factors in hemodialysis (HD) patients. Methods: Patients aged 30-70 years undergoing HD between 2015 to 2019 were enrolled in this cross-sectional study. BMD measured by dual energy x-ray absorptiometry (DEXA) and biochemical laboratory tests were assessed in 200 patients undergoing HD. Statistical analysis was based on t test, Pearson, regression and Mann-Whitney tests using SPSS 16. Results: Two hundred patients were investigated. Sixty percent of the patients were female. Mean ± SD of participants’ age was 58.6 (±11.63) years and mean ± SD for duration of HD was 45.69 (± 43.76) months. Osteoporosis was found in 48% (n=96) and low bone density in 36% (n=76) of our patients. General osteoporosis was more frequent in those undergoing HD for more than 3 years, although not significantly (P=0.093, odds ratio [OR]=0.37). However, regional osteoporosis in hip and femoral neck, but not spine vertebrae, were significantly higher after three years of HD (P=0.036, OR=0.27; P=0.042, OR=0.27; and P=0.344, OR=0.56, respectively). Increased body mass index (BMI) correlated negatively with osteoporosis (P=0.050). Conclusion: With increasing age and duration of HD, BMD decreases. Higher BMI was associated with higher bone mass density. Bone density assessment seems to be necessary in patients undergoing HD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".