Management of Iron-Deficiency Anemia on Inpatients and Appropriate Discharge and Follow-Up
Bibliographic record
Abstract
BACKGROUND: The aims of the study were to identify appropriate supplementation of iron for inpatients and to identify factors involved in appropriate discharge documentation and follow-up. METHODS: This was a retrospective analysis of 103 patients at a community hospital in New York City. RESULTS: A total of 57 (57/103, 55.3%) patients were admitted due to symptomatic anemia. Twenty (20/103, 19.4%) of those with iron-deficiency anemia had either esophagogastroduodenoscopy or colonoscopy. Gastroenterologist or hematologist was consulted for 45/103 (43.7%). Inpatient iron supplementation was given for 62/103 (60.2%) of patients; and 43/103 (41.7%) had blood transfusion. Upon discharge, 50/103 (48.5%) had appropriate documentation of iron-deficiency anemia on discharge paperwork. Appropriate follow-up was done for 54/103 (52.4%). Iron supplementation was provided for 53/103 (51.5%) of patients. Having inpatient esophagogastroduodenoscopy or colonoscopy, blood transfusion, or symptomatic anemia had a statistical significance for likelihood of appropriate discharge documentation. CONCLUSIONS: Iron-deficiency anemia can have high rates of mortality and morbidity in the population. Appropriate discharge of patients with iron-deficiency anemia and factors related to this are paramount for clinicians in order to have the best patient outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".