Bibliographic record
Abstract
BACKGROUND: The aim of the study was to determine the prevalence of anemia in patients with type 2 diabetes and to assess the risk of anemia according to gender, age and glycemic control. METHODS: The study group comprised of patients with type 2 diabetes attending Outpatient Diabetic Department of Amiri Hospital (Al-Asimah Capital area) from January 1, 2016 to December 31, 2017. Patients were divided into groups according to glycemic status and gender. Glycated hemoglobin (HbA1C) values and hemoglobin (Hb) levels were evaluated. The presence of anemia was defined by an Hb level < 13.0 g/dL for men and < 12.0 g/dL for women. RESULTS: The prevalence of anemia is significantly greater in diabetic females (38.5%) than in diabetic males (21.6%) and in poorly controlled diabetics (33.46%) than those with glycemic status under control (27.9%) (P < 0.05). The average age of patients with anemia was found to be 60.69 ± 0.198 years and the average age of patients without anemia was found to be 54.07 ± 0.121 years. This indicates that the risk of anemia increases with age. CONCLUSION: Screening for anemia at the time of diagnosis of diabetes, diabetic medication compliance, awareness of the risk of anemia and other complications in the diabetic patients helps in reducing the prevalence of anemia in diabetic population.
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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".