Improving quality of care for patients with iron deficiency anemia presenting to the emergency department
Bibliographic record
Abstract
BACKGROUND: Patients presenting to the emergency department (ED) with iron deficiency anemia (IDA) are underrecognized, undertreated with iron, and overtransfused. A 3-month audit of red blood cell (RBC) transfusions at the Sunnybrook Health Sciences Centre ED in 2013 showed that only 53% of transfusions for IDA were appropriate. The aim of this quality improvement project was to increase the rate of appropriate transfusion to greater than 80%. STUDY DESIGN AND METHODS: Since November 2013, several quality improvement interventions have been implemented, including educational presentations, development of an algorithm on IDA management in the ED, and development of an ED IDA toolkit. The primary outcome was appropriateness of RBC transfusions per month. The process measure was monthly intravenous (IV) iron use in IDA patients managed exclusively by ED staff. Balancing measures included IV iron use according to the algorithm and undertransfusion. RESULTS: Over a 24-month period beginning January 2014, assessment of 193 units transfused in the ED showed an improvement of RBC appropriateness to 91% (range 50%-100%). IV iron use increased from one dose in the 3-month audit to an average of 2.6 and 4.7 per month in 2014 and 2015, respectively. IV iron use did not follow the algorithm in 19% (18 of 93) of cases: 12 were given to patients with less severe iron deficiency or bleeding. CONCLUSION: Improved RBC transfusion appropriateness for IDA in the ED can be achieved and maintained with the implementation of simple educational and practical interventions.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".