Red blood cell transfusion and the use of intravenous iron in iron‐deficient patients presenting to the emergency department
Bibliographic record
Abstract
Background and aims: Red blood cell transfusions are often used to treat iron-deficient patients in the emergency department (ED), while treatment with intravenous (IV) iron is preferred, as it increases hemoglobin concentration rapidly and durably. We aim to evaluate the incidence of iron deficiency anemia, frequency of blood transfusion and iron supplementation, and factors associated with blood transfusion in the ED. Methods: We conducted a retrospective cohort study of adult patients presenting to the St. Boniface Hospital (Winnipeg, Canada) ED from 2014 to 2018. Electronic data obtained from the Emergency Department Information System and Laboratory Information Services databases identified patients presenting with iron deficiency anemia, defined as microcytic (mean corpuscular volume < 75 fL) anemia (hemoglobin < 120 g/L) with either a transferrin saturation <20% or ferritin < 30 µmol/L. Ferritin > 100 µmol excluded iron deficiency anemia. The use of blood transfusions or iron supplementation was determined for each patient. Factors associated with blood transfusion were determined using logistic regression analyses. Results: Of 39,222 patients, 17,945 (45%) were anemic. In anemic patients, iron parameters were ordered in 1848 (10.3%) and iron deficiency anemia was diagnosed in 910 (5.1%). Ninety-five patients (10.4%) received one red blood cell unit, and 197 patients (21.6%) received ≥2 units. Oral iron and IV iron were prescribed for 64 (7.0%) and 14 (1.5%) patients, respectively. Hemoglobin concentration was the main determinant for treatment with blood transfusion. Conclusions: Iron deficiency is underinvestigated among anemic patients presenting to the ED. The only clinical factor associated with red blood cell transfusion in the ED was hemoglobin level, irrespective of symptoms or clinical stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".