Prevalence of anemia and its associated factors among patients with type 2 diabetes mellitus in the north of Iran
Bibliographic record
Abstract
Abstract Background: Purpose: This study intended to investigate the prevalence of anemia and its associated factors among patients with type 2 diabetes mellitus (T2DM) in Gorgan, North of Iran.Methods: This cross-sectional study was performed on 415 (109 men) patients with T2DM referred to Gorgan Diabetes Clinic in 2021. Demographic information was collected and some laboratory tests such as cell counts, serum blood glucose, creatinine, lipid and iron profiles, and urinary albumin were performed. Multivariable logistic regression analysis was applied to compute odds ratios (ORs) and 95% confidence intervals (CI) for potential associated factors, using SPSS version 21.Results: The prevalence of anemia was about 21% among our participants. The adjusted model revealed that obesity (OR, 1.94 [95% CI, 1.17-1.23]), T2DM duration for more than five years (OR, 3.12 [CI, 1.78-5.47]), albuminuria (OR, 6.37 [CI, 3.13-10.91]), chronic kidney disease (OR, 4.30 [CI, 2.83-8.29]) and high triglycerides (OR, 1.72 [CI, 1.21-2.77]) were significantly associated with anemia among patients with T2DM. Moreover, using insulin both with (OR, 2.60 [CI, 1.42-6.42]) and without (OR, 1.87 [CI, 1.30-4.37]) oral glucose-lowering medications had a positive association with prevalence of anemia among our participants.Conclusion: Anemia had a high prevalence among patients with T2DM in the north of Iran which is associated with obesity and hypertriglyceridemia, duration and severity of T2DM, and diabetic kidney disease.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".