Prevalence of anemia and its associated factors among patients with type 2 diabetes mellitus in a referral diabetic clinic in the north of Iran
Bibliographic record
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, Iran. METHODS: This cross-sectional study was conducted on 415 (109 men) patients with T2DM referred to the referral diabetes clinic of Sayad Shirazi Hospital in Gorgan in 2021. Demographic information, anthropometric indices, past medical history, and some laboratory data on cell counts, serum blood glucose, HbA1c, creatinine, lipid/iron profiles, and urinary albumin were collected. The univariable and multivariable logistic regression analysis was applied to compute odds ratios (ORs) and 95% confidence intervals (CI) for potential associated factors, using SPSS version 21. The multivariable Model was adjusted for obesity, Hb A1c, T2DM duration, using glucose-lowering drugs (GLDs), chronic kidney disease (CKD), albuminuria, hypertriglyceridemia, and hypercholesterolemia. RESULTS: The prevalence of anemia was 21.5% [95%CI: 17.6-25.7] among our total participants. The corresponding values for men and women were 20.2 (13.1-29.0) and 21.9 (17.4-27.0), respectively.The adjusted model revealed that obesity (OR, 1.94 [95% CI, 1.17-3.23]), T2DM duration for more than five years (OR, 3.12 [1.78-5.47]), albuminuria (OR, 6.37 [3.13-10.91]), chronic kidney disease (OR, 4.30 [ 2.83-7.29]) and hypertriglyceridemia (OR, 1.72 [ 1.21-2.77]) were significantly associated with prevalent anemia among patients with T2DM. Moreover, using insulin separately or in combination with oral GLDs associated positively with the prevalence of anemia with ORs of 2.60 [1.42-6.42] and 1.87 [1.30-4.37] , respectively. CONCLUSION: Anemia had a high prevalence among patients with T2DM in the north of Iran (about 22%), which is associated with obesity, hypertriglyceridemia, duration 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 | 0.001 | 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 | 0.000 | 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".