Evaluation of Frequency and Type of Severe Anemia in Patients Referred to the Baqiyatallah Hospital in Tehran in Six Months; A Descriptive Cross-Sectional Study
Bibliographic record
Abstract
Purpose: To investigate the frequency and types of severe unknown anemia in patients referred to the Baqiyatallah Hospital (Tehran) for six months. Methods: In this descriptive cross-sectional study, the patients with severe unknown anemia referred to the Baqiyatallah Hospital (Tehran, Iran) were selected over six months. Following consideration of inclusion and exclusion criteria, 230 patients with severe anemia (hemoglobin (Hb) > 8gr/dl) were included. Complete medical history was obtained from the patients and additional biochemical blood analyses were applied to determine the frequency and type of anemia. SPSS (v.19) software was used to analyze the findings and the significance level was defined as a p-value <0.05. Results: In chronic disease anemia (47.5%), gastrointestinal bleeding-associated anemia (29%), bleeding malignancies anemia (21.5%), and aplastic anemia (2%). There were significant differences (p<0.05) in the frequency of different types of normocytic anemia. The highest frequency was detected in folate deficiency anemia (46%), hypothyroidism anemia (34%), and B12 deficiency anemia (20%), respectively. The hemolytic anemia represented a significant difference (p<0.05) in comparison with sickle cell anemia (95%). Also, sickle cell anemia showed a significant difference (p<0.05) between thalacemia-associated anemia (95%) and malignancy-related anemia (95%) Conclusion: Respectively, the highest frequency of anemia in patients was found in chronic diseases and gastrointestinal bleeding. It is suggested that more attention should be paid to the type of anemia of patients referred to the urgency of hospitals.
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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".